Building model with capture of as built features and experiential data

ABSTRACT

An augmented virtual model of a commercial structure, such as a processing plant and/or a manufacturing plant which includes a virtual design, as built data and experiential measurement data. A User may view the augmented model in a virtual reality setting based with the content presented to the user based upon a location of the user at the time of viewing. Aspects of the Augmented Virtual Model are updated over time and utilized to track performance.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims priority to Non Provisional patent application Ser. No. 15/703,310, filed Sep. 13, 2017 and entitled BUILDING MODEL WITH VIRTUAL CAPTURE OF AS BUILT FEATURES AND OBJECTIVE PERFORMANCE TRACKING as a Continuation in Part application: and to Non Provisional patent application Ser. No. 15/716,133, filed Sep. 29, 2017 and entitled BUILDING MODEL WITH VIRTUAL CAPTURE OF AS BUILT FEATURES AND OBJECTIVE PERFORMANCE TRACKING as a Continuation in Part application: and to Provisional Patent Application Ser. No. 62/462,347, filed Feb. 22, 2017 and entitled VIRTUAL DESIGN, MODELLING AND OPERATIONAL MONITORING SYSTEM: and to Provisional Patent application Ser. No. 62/531,955, filed Jul. 13, 2017 and entitled BUILDING MODELING WITH VIRTUAL CAPTURE OF AS BUILT FEATURES; and to Provisional Patent Application Ser. No. 62/531,975 filed Jul. 13, 2017 and entitled BUILDING MAINTENANCE AND UPDATES WITH VIRTUAL CAPTURE OF AS BUILT FEATURES; the contents of each of which are relied upon and incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates to methods and apparatus for creating models including virtual design and operation of a facility and capturing actual build details and performance of a facility modeled. More specifically, the present invention presents methods and apparatus for generating an immersive experience in a virtual facility that is capable of emulating a physical facility. As described herein, a facility may include one or more of: a commercial building, a processing plant and a manufacturing plant.

BACKGROUND OF THE INVENTION

Traditional methods of using automated design tools, such as AutoDesk™, are focused on the generation of a design plan for use in construction of a facility, such as a processing plant. An automated design tool may be advantageous in the specifying of building aspects, materials and placement of features. Aspects may include building features, such as walls, ingress/egress, utilities and even equipment.

More sophisticated productions include three dimensional models viewable from one or more user selected vantage points within the model. However, in general, the automated modeling ends with the construction of the facility that has been modeled. It may be desirable to have ability to operate a design system that provides virtual modelling of facility aspects from the site level all the way down to the operational aspects of work material flow within equipment in the facility and there between. It may be further desirable where such a virtual modelling system may be supportive of operational monitoring systems on physically operating facilities where virtual modelling may be compared to actual results in a displayed format.

Such traditional methods of design of a Processing Facility are primarily accomplished based upon a predefined objective. For example, a factory seeking a certain level of performance, such as a production rate, may be designed with an appropriate layout and include machinery with sufficient size and speed to meet the level of production sought. Design models are not able to quantify whether an intended level of performance has been met because they are not equipped with a means to capture empirical data on an ongoing basis.

Similarly, while traditional methods of using automated design tools, such as AutoDesk™, have greatly increased the capabilities of virtual models of facilities, very little has been done to quantify a deployed performance of design features, such as equipment layout, capacity, throughout consumables walls, ingress/egress, windows, ceiling designs, textures, building materials, placement of structural beams, utilities, machinery location, machinery type, machinery capacity equipment.

More sophisticated design systems include “virtual reality” models. Virtual reality models may include two dimensional and/or three dimensional views from one or more user selected Vantage Points within the model of the structure. However, in general, access to the automated modeling by support personnel ends upon the construction of the building that has been modeled. Hard copy prints of a commercial building may be available; however there is no guarantee that a building was constructed according to design plans or which equipment and machinery will be encountered.

SUMMARY OF THE INVENTION

Accordingly, the present invention combines methods and apparatus into systems that extend the usefulness of a model of a facility through the life of the facility constructed and relates the model with real time and historical data. The present invention draws upon designs, materials, equipment deployed, environmental conditions experienced and other tangible conditions to provide a user with one or more factors including: total cost of ownership of a facility over the life of the facility or a specified span of years, total throughput, Quality of Goods Processed, Total Yield, Total human resources required to run the facility, and other factors that are vital to successful operation of a facility. The present invention additionally provides that the factors listed above may be calculated in a model and validated with real time and/or historical data.

The present invention provides for automated apparatus for improved modeling of construction, Deployment and updating of a Processing Facility. The improved modeling is based upon generation of As Built and Experiential Data captured with one or both of Smart Devices and Sensors located in or proximate to the Processing Facility. The automated apparatus is also operative to model compliance with one or more performance levels for the Processing Facility related to processing of a Commercial Product.

User input Performance Metrics may include an allocation of an amount of consumables required for a level of Performance a Processing Facility will experience during Deployment of the Processing Facility. Design, repair, maintenance and upgrades to a Processing Facility are modeled with the automated apparatus by incorporating “As Built” data that is descriptive of a physical structure and “experiential” data derived from experiential sensor readings of sensors located within or proximate to the physical structure. Sensors may be included as part of a Processing Facility Structure and/or transported by a User. The data is incorporated into a virtual model of the structure and used to determine Performance Level.

In another aspect of the present invention, a virtual model of a Processing Facility extends beyond a design stage of the structure into an ‘As Built” stage of the structure and additionally includes generation and analysis of Experiential Data capturing conditions realized by the Processing Facility during a Deployment stage of the structure.

In general, As Built and Experiential Data generated according to the present invention includes one or more of: image data, measurements, component specifications of placement; solid state; electrical; and electromechanical devices (or combination thereof); generate data capturing conditions experienced by a structure. In addition, a user may enter data, such as for example, data descriptive of an action taken by a service technician into the AVM. As Built and Experiential Data may be aggregated for a single structure or multiple structures. A Likewise, a Processing Facility may comprise a single structure or multiple structures.

In one aspect, an AVM virtual model may receive as input one or more stated Performance Levels for which the Processing Facility is deployed to meet. As Built data and Experiential Data are generated and analyzed for predicting a rate of success of achieving a stated Performance Level and quantifying a rate of success of the stated Performance Level.

As Built data is collected that quantifies details of how a specific physical structure was actually constructed. According to the present invention, a Processing Facility is designed and modeled in a 3D virtual setting. As Built data is combined with a design model in a virtual setting to generate an AVM. As Built data may reflect one or more of: fabrication of the Processing Facility; repair; maintenance; upgrades; improvements; and work order execution associated with the Processing Facility.

In addition, Experiential Data may be generated and entered into the AVM virtual model of the structure. Experiential Data may include data indicative of a factor that may be tracked and/or measured in relation to the Processing Facility. Experiential data is typically generated by Sensors in or proximate to the Processing Facility and may include, by way of non-limiting example, one or more of: vibration Sensors (such as accelerometers and piezo electro devices); force transducers; temperature sensing devices; amp meters, ohmmeters, switches, motion detectors; light wavelength capture (such as infrared temperature profile devices), water flow meters; air flow meters; and the like. Some examples of Experiential Data may include: details of operation of equipment or machinery in the Processing Facility; vibration measurements; electrical current draws; machine run times, machine run rates, machine run parameters; interior and/or exterior temperatures; opening and closings of doors and windows; weight loads; preventive maintenance; cleaning cycles; air circulation; mold contents; thermal profiles and the like. Automated apparatus captures empirical data during construction of the Processing Facility and during Deployment of the Processing Facility.

In some embodiments, empirical data may be used to track achievement of a defined Performance Level. Empirical data may also be used schedule Deployment of assets for the Processing Facility. Deployment of assets may include one or more of: human resources in the form of man hours; equipment, consumer devices, fixtures, machinery, maintenance items and the like. Deployment of assets may also include tracking of pecuniary equivalents of assets or other fungible equivalent.

In preferred embodiments, dedication of one or more assets for which a value may be associated, is translated into a modification of the Processing Facility. A return on investment for a modification may be captured via the automated apparatus as additional empirical data. Empirical data may also be used to increase the accuracy of virtual models generated by the automated apparatus that are descriptive of the Processing Facility. A return on investment (“ROI”) may be measured according to variables involved, for example, increased energy efficiency resulting from preventative maintenance procedures may be measured according to an amount of power required for similar Performance pre and post the PM procedure, or fewer user complaints. In another aspect, return on investment may be translated to a fungible item, such as currency or other financial amount, in order to best track disparate variables on a comparative basis.

By way of additional example, it may be determined that water consumption in a particular Processing Facility, or a particular class of processing plants, will be analyzed to determine if it is prudent to make modifications to the particular Processing Facility or class of processing plants. The automated apparatus of the present invention will include As Built data for features of a structure that is accessed while modeling proposed modifications and upgrades. Relevant As Built Features may include features for which relevancy may seem obvious, such as, for example, one or more of: utility requirements, electrical, chemical supply, chemical waste disposal, air handling equipment, hoods, exhaust and filtering; plumbing; machinery models and efficiency. In addition, other As Built Features, for which relevancy may not seem obvious, but which unstructured queries draw a correlation may also be included. Location of machinery relative to other machinery may also be deemed relevant by unstructured query analysis. An unstructured query of captured data quantifying actual chemical, atmosphere and water usage may determine that certain configurations better meet an objective than others. It may later be determined that the single story structure is more likely to have a consistent internal temperature, lighting, ambient particulate or other trends.

As discussed more fully below, captured data may include empirical quantifications of a number of times a piece of machinery cycles on and off, vibrations within a structure, temperature within a structure, doors opening and closing, quantity of products processed, hours of occupancy of the structure and other variable values. Captured data may also be used to generate a determination of how a structure is being used, such as production cycles, quality, yield, rates, volumes, etc. As discussed more fully below, empirical Sensor data associated with how particular personnel behaves within a Processing Facility may also be correlated with structure Performance based upon who occupies a particular structure, when they occupy and for how long.

The automated apparatus combines a model of a structure that has been designed and provides precise additions to the model based upon data capture of features actually built into the structure. Service calls that may include one or more of: repairs, upgrades, modifications and additions (hereinafter generally referred to as “Service Call”), may access data indicating an AVM combined with precise features included in a building represented by As Built data, as well as Experiential Data and technical support for the features, maintenance logs and schedules, “how to” documentation and video support, virtual connection to specialists and experts, and a time line of original As Built details and subsequent modifications. Modifications may include repairs, updates and/or additions to a structure.

The improved methods taught herein provide for the performance of repairs, maintenance and upgrades via access to a system that incorporates “As Built” data into the AVM. Geolocation and direction will be used to access virtual reality representations of a structure including actual “As Built Imagery” incorporated into the AVM that accurately indicates locations and types of features and also provides images or other captured data. Exemplary data may include As Built locations of structural components (beams, headers, doorways, windows, rafters etc.); HVAC, electrical, plumbing, machinery, equipment, etc. Virtual repair may include “how to” instructions and video, technical publications, aggregated of similar repair orders and the like. An onsite technician may verify correct location of an equipment unit based upon GPS, triangulation, direction determinations.

A virtual reality model may additionally include virtual operation of equipment and use of modeled structure based upon aggregated data from one or more As Built structures. Upon conclusion of a repair, maintenance, upgrade or addition. Additional information quantifying time, place, nature of procedure, parts installed, equipment, new component location etc. may be captured and incorporated into a virtual model.

Some embodiments of the present invention include capturing data of procedures conducted during preventive maintenance and/or a Service Call and inclusion of relevant data into a virtual model. Precise data capture during a Service Call or during construction may include actual locations of building features such as, electrical wiring and components, plumbing, joists, headers, beams and other structural components. Data capture may be ongoing over time as the building is used and modified, or updated during the life of a structure (sometimes referred to herein as the “Operational” or “Deployed” stage of the structure).

An Operational Stage may include, for example: occupation and use of a Commercial Property, as well as subsequent modifications, repairs and structure improvements. The Commercial Property may include one or more modeled structures, such as: a factory, processing plant, fabrication facility, server farm, power generator facility, an outbuilding and facilities included in a Commercial Property. Smart Devices with unique methods of determining a location and direction of data capture are utilized to gather data during construction of modeled buildings or other structures and during Deployment of the structures during the Operational Stage.

In general, Smart Devices provide ongoing collection of “As Built” and “Deployed” data that is captured during construction and Deployment of a commercial building. The collected data is further correlated with design data and used to track Performance of features included in a design of process plants and/or features included within the confines of a Commercial Property parcel (“Commercial Property”).

In another aspect, collected data may be used to predict Performance of a Commercial Property based upon features built into the structure and conditions experienced by the Commercial Property. As Built data may include modifications to a Commercial Property that are made during a construction phase, and/or during a Deployment phase, of a Commercial Property life cycle. Similarly, as Deployed data may include details quantifying one or more of: machine operators, production quantity, yield, quality level, usage, maintenance, repairs and improvements performed on the Commercial Property.

In still another aspect of the present invention, predictive analytics may be performed to predict a life of various components included in the Commercial Property. Maintenance procedures and replacement of consumables or other parts may also be budgeted and scheduled based upon a correlation of a) design data; b) As Built data; and c) as used data. In addition, contemplated improvements may be modeled according to an expected return on investment (“ROI”). An expected ROI may be calculated according to one or more of: an objective level of measurements, an amount of a fungible item, such as kilowatt, gallon, currency, volume or other quantity expended during the life of Deployment; and satisfaction of users and Performance.

Predictive analytics may include monitoring use of equipment and machinery. The monitoring may include data collection that is stored in a controller and analyzed, such as, via artificial intelligence routines. In some embodiments, data gathered during monitoring may be transmitted to a centralized location and aggregated with other similar type buildings, equipment and machinery. Analytic profiles may be generated. Predicted Performance and failures may be generated and used to schedule Service Calls before a physical failure occurs. Profiles may include degrees of usage, consumables, electric current draws, vibration, noise, image capture and the like.

In some embodiments, production rates, yields, cost of build, and cost of Deployment, including maintenance costs incurred during Deployment of a Commercial Property may be calculated and included into one or more of: a production value of a Commercial Property including a Processing Facility; a sale price of a Commercial Property; and a lease value of a Commercial Property and overall asset volume of the Commercial Property.

In still another aspect, a comprehensive cost of build and Deployment may be amortized over a term of years. Still further, an amortized cost may be included in a scheduled payment for a term of years, such as, for example a monthly mortgage payment, wherein the monthly mortgage payment includes Total Cost of Ownership. Total Cost of Ownership may include one or more of acquisition, deployment, repair and maintenance and energy usage. In some respects, a sale price that includes Total Cost of Ownership may have favorable tax implications for one of or both Buyer and Seller.

Still another aspect includes generation of virtual reality user interfaces accessing the AVM based upon a) design data; b) As Built data; c) as used data; and d) improvement data. A virtual reality user interface may be accessed as part of one or more of: a maintenance routine; to support a change order for the Commercial Property; and to contemplate improvements in a Commercial Property. As Built and as deployed data may include data quantifying repairs and updates to the Commercial Property.

In some embodiments, a) design data; b) As Built data; c) Experiential Data; and d) Lead Actions and Lag Benefit measurements, as they relate to multiple Commercial Properties may be aggregated and accessed to support one or more Commercial Properties. Access to aggregated data may include execution of artificial intelligence (AI) routines. AI routines may include, by way of non-limiting example; structured algorithms and unstructured queries operative to predict Performance metrics and maintenance needs. AI routines may access both initial designs and data aggregated during build and deployment stages of the Commercial Property.

The details of one or more examples of the invention are set forth in the accompanying drawings and the description below. The accompanying drawings that are incorporated in and constitute a part of this specification illustrate several examples of the invention and, together with the description, serve to explain the principles of the invention: other features, objects, and advantages of the invention will be apparent from the description, drawings, and claims herein.

DESCRIPTION OF THE DRAWINGS

The accompanying drawings, that are incorporated in and constitute a part of this specification, illustrate several embodiments of the invention and, together with the description, serve to explain the principles of the invention:

FIG. 1A illustrates a block diagram of inter-relating functions included in automated systems according to the present invention.

FIG. 1B illustrates geolocation aspects that may be used to identify a Commercial Property and corresponding data and predictions.

FIG. 1C illustrates a block diagram of ongoing data capture via Smart Devices and Sensors and support for predictive modeling based upon the smart data capture.

FIG. 1D illustrates an exemplary Progressive Facility layout with various equipment delineated in a top-down representation according to some embodiments of the present invention.

FIG. 1E illustrates a diagram of a user and directional image data.

FIG. 2 illustrates a block diagram of an Augmented Virtual Modeling system.

FIGS. 3A-3F, are illustrations of exemplary aspects of collecting and displaying data of a Processing Facility generated during construction of the Processing Facility.

FIG. 3G illustrates an exemplary key component of the model system, with a Performance monitor providing data via a communication system to the model system.

FIG. 3H illustrates an exemplary virtual reality display in concert with the present invention.

FIGS. 4A, 4B, and 4C illustrate an exemplary method flow diagrams with steps relating to processes.

FIG. 5 illustrates location and positioning devices associated within a Processing Facility.

FIG. 6 illustrates apparatus that may be used to implement aspects of the present invention including executable software.

FIG. 7 illustrates an exemplary handheld device that may be used to implement aspects of the present invention including executable software.

FIG. 8 illustrates method steps that may be implemented according to some aspects of the present invention.

FIGS. 9A-D illustrates views of an AVM via a wearable eye display according to some aspects of the present invention.

FIGS. 10A-C illustrates viewing areas of an AVM according to some aspects of the present invention.

FIGS. 11A-C illustrates vertical changes in an AVM viewable area according to some aspects of the present invention.

FIG. 12 illustrates designation of a direction according to some aspects of the present invention.

DETAILED DESCRIPTION

The present invention relates to methods and apparatus for improved modeling, Deployment and updating of commercial Processing Facility based upon As Built and Experiential Data. As Built and Experiential Data may quantify an allocation of resources required for a level of Processing Facility Performance during Deployment of the facility. Design, repair, maintenance and upgrades to a Processing Facility are modeled with an automated system that incorporates “As Built” data and “Experiential” data into a virtual model of the structure to determine a level of performance of the Processing Facility.

The present invention provides automated apparatus and methods for generating improved Augmented Virtual Models (sometimes referred to herein as an “AVM”) of a Processing Facility; the improved AVMs are capable of calculating a likelihood of achieving stated Performance Level specified by a user. In addition, the improved model may be operative to generate target Performance Metrics based upon As Built and Experiential Data.

The Augmented Virtual Model of the Commercial Property may include a conceptual model and progress through one or more of: a) a design stage; b) a build stage; c) a Deployment stage; d) a service stage; e) an modification stage; and f) a dispensing stage. As discussed more fully herein, an AVM according to the present invention include original design data matched to As Built data captured via highly accurate geolocation, direction and elevation determination. As Built data is matched with a time and date of data acquisition and presented in two dimensional (2D) and three dimensional (3D) visual representations of the Commercial Property. The augmented models additionally include data relating to features specified in a Commercial Property design and data collected during building, Deployment, maintenance and modifications to the Commercial Property. In some embodiments, a fourth dimension of time may also be included.

An Augmented Virtual Model includes a three or four dimensional model in a virtual environment that exists parallel to physical embodiments modeled in the Augmented Virtual Model. Details of one or more physical structures and other features within a commercial real estate parcel are generated and quantified and represented in the Augmented Virtual Model. The Augmented Virtual Model exists in parallel to a physical structure in that the AVM includes virtual representations of physical structures and additionally receives and aggregates data relevant to the structures over time. The aggregation of data may be one or more of: a) according to an episode (i.e. onsite inspection, repair, improvement etc.); b) periodic; and c) in real time (without built in delay).

The experience of the physical structure is duplicated in the virtual Augmented Virtual Model. The Augmented Virtual Model may commence via an electronic model generated via traditional CAD software or other design type software. In addition, the AVM may be based upon values for variables, including one or more of: usage of a structure; usage of components within the structure; environmental factors encountered during a build stage or Deployment stage; and metrics related to Performance of the structure. The metrics may be determined, for example, via measurements performed by Sensors located in and proximate to structures located on the Commercial Property.

In another aspect, an Augmented Virtual Model may be accessed in relation to modelling achievement of a stated Performance Level. Accurate capture of As Built Features and aggregated data of similar buildings, equipment types, machinery and usage profiles assist in one or more of: predicting Performance Level, Yield, Quality, Volume of Production, selecting appropriate technicians to deploy to a service call; providing correct consumables and replacement parts, scheduling a preventative maintenance; scheduling building, equipment and/or machinery upgrades; matching a building, equipment and machinery combination of a particular type of Deployment; providing on site guidance during the Service Call; providing documentation relevant to the building, equipment and machinery; providing access to remote experts that guide onsite technicians.

In some embodiments, a technical library specific to a particular property and location within the property may be maintained for each Commercial Property and made accessible to an onsite technician and/or remote expert. The library may include, but is not limited to: structure, equipment/machinery manuals; repair bulletins, and repair/maintenance. Appropriate how to videos may also be made available based upon an AVM with As Built and Experiential Data.

In another aspect, a parts ordering function may be included in the Augmented Virtual Model. Augmented parts ordering may allow a technician to view an ordered part and view a virtual demonstration of the part in use and procedures for replacing the part.

Aspects of the Augmented Virtual Model may be presented via a user interface that may display on a tablet or other flat screen, or in some embodiments be presented in a virtual reality environment, such as via a virtual reality headset.

The present invention additionally provides for an Augmented Virtual Model to forecast Future Performance of a Commercial Property based upon the values of variables included in data aggregated during the design, build and Deployment of the Commercial Property sometimes referred to herein as: a) Design Features; b) As Built data; and c) as Deployed data.

The improved modelling system incorporates “As Built” data into the improved design model. Subsequently, an onsite or remote technician may access the As Built data to facilitate. The As Built data is generated and/or captured via highly accurate geolocation, direction and elevation determination. Based upon the geolocation, direction and elevation determination, As Built data is incorporated into a design model at a precise location within the AVM. In some embodiments, a time and date of data acquisition may be associated with updates to aspects of the improved AVM such that a chronology of changes exists within the AVM.

Original design aspects and updated design aspects may be presented in two dimensional (2D) and three dimensional (3D) visual representations of the Commercial Property. The present invention provides for systematic updates to As Built data during a Deployment of the Commercial Property. Updated data may verify and/or correct previously included data and also be used to memorialize modifications made during a Service Call or modification to a Commercial Property.

Some exemplary embodiments may include updates to an AVM that include, one or more of: quantifying a make and model of equipment and machinery on site; time and date notation of change in location specific data; Model accessed and/or updated according to XYZ and distance data; XY data may include high level location designation within the street address via triangulation (i.e. such as a street address) and highly specific position designation (i.e. particular room and wall); combination of two types of position data; GPS, Differential GPS; references used during triangulation; aggregate data across multiple structures for reference; designs that perform well; designs that fail; popularity of various aspects; access to and/or generation of, multiple Augmented Virtual Models; original and modified model versions; index according to date/time stamp; index according to feature; index according to popularity; index according to cost; index according to User specific query; plumbing; electrical; HVAC; chemical, raw material, structural; access areas (i.e. crawl spaces, attics); periodic data and position capture with camera/Sensor attached to a fixed position; and during one or more of: repair/maintenance/updates.

Accordingly, actual “As Built’ imagery and location data is incorporated into the design model to accurately indicate a location and type of feature included in a structure, and provide “pictures” or other captured data. Exemplary data may include As Built locations of structural components (beams, headers, doorways, windows, rafters etc.); HVAC, electrical, plumbing, machinery, equipment, etc. A virtual reality model may additionally include virtual operation of machinery and equipment and use of a Processing Facility based upon aggregated data from the structure, as well as annotations and technical specifications relating to features included in the As Built model of a Processing Facility identified by time, date, geolocation and direction.

In some embodiments, an initial digital model may be generated according to known practices in the industry. However, unlike previously known practices, the present invention associates an initial digital model with a unique identifier that is logically linked to a geolocation and one or both of date and time designation, and provides updates to the original model based upon data captured at the geolocation during a recorded timeframe. In this manner, a Virtual Reality Simulation is generated that logically links a digital model to a specific geographic location and actual As Built data at the specific geographic location. The updated model may be virtually accessed from multiple locations such as a field office, onsite, a technical expert, a financial institution, or other interested party.

In some preferred embodiments, the geographic location will be provided with accurately placed location reference points. The location reference points may be accessed during activities involved in a Service Call on the Commercial Property, such as a repair or upgrade to a structure or other structures included within a property parcel surrounding the structure. Accuracy of the reference points may or may not be associated with location relevance beyond the Commercial Property, however they do maintain accuracy within the Commercial Property.

Preferred embodiments may also include reference points accurately placed within a structure Processing Facility located on the Commercial Property. As further discussed below, the reference points may include, by way of non-limiting example, a wireless transmission data transmitter operative to transmit an identifier and location data; a visual identifier, such as a hash code, bar code, color code or the like; an infrared transmitter; a reflective surface, such as a mirror; or other means capable of providing a reference point to be utilized in a triangulation process that calculates a precise location within the structure or other structure.

Highly accurate location position may be determined via automated apparatus and multiple levels of increasingly accurate location determination. A first level may include use of a GPS device providing a reading to first identify a Commercial Property. A second level may use position transmitters located within, or proximate to, the Commercial Property to execute triangulation processes in view of on-site location references. A GPS location may additionally be associated with a high level general description of a property, such as, one or more of: an address, a unit number, a lot number, a taxmap number, a county designation, Platte number or other designator. On-site location references may include one or more of: near field radio communication beacons at known X-Y position reference points; line of sight with physical reference markers; coded via ID such as Bar Code, Hash tag, and alphanumeric or other identifier. In some embodiments, triangulation may calculate a position within a boundary created by the reference points to within millimeter range. In some embodiments, Differential GPS may be used to accurately determine a location of a Smart Device with a sub centimeter accuracy.

In addition to a position determination, such as latitude and longitude, or other Cartesian Coordinate (which may sometimes be indicated as an “X and Y” coordinate) or GPS coordinate, the present invention provides for a direction (sometimes referred to herein as a “Z” direction and elevation) of a feature for which As Built data is captured and imported into the AVM.

According to the present invention, a direction dimension may be based upon a movement of a device. For example, a device with a controller and an accelerometer, such as mobile Smart Device, may include a user display that allows a direction to be indicated by movement of the device from a determined location acting as a base position towards an As Built feature in an extended position. In some implementations, the Smart Device may first determine a first position based upon triangulation with the reference points and a second position (extended position) also based upon triangulation with the reference points. The process of determination of a position based upon triangulation with the reference points may be accomplished, for example via executable software interacting with the controller in the Smart Device, such as, for example via running an app on the Smart Devices.

In combination with, or in place of directional movement of a device utilized to quantify a direction of interest to a user, some embodiments may include an electronic and/or magnetic directional indicator that may be aligned by a user in a direction of interest. Alignment may include, for example, pointing a specified side of a device, or pointing an arrow or other symbol displayed upon a user interface on the device towards a direction of interest.

In a similar fashion, triangulation may be utilized to determine a relative elevation of the Smart Device as compared to a reference elevation of the reference points.

It should be noted that although a Smart Device is generally operated by a human user, some embodiments of the present invention include a controller, accelerometer, data storage medium, Image Capture Device, such as a Charge Coupled Device (“CCD”) capture device and/or an infrared capture device being available in a handheld or unmanned vehicle.

An unmanned vehicle may include for example, an unmanned aerial vehicle (“UAV”) or ground level unit, such as a unit with wheels or tracks for mobility and a radio control unit for communication.

In some embodiments, multiple unmanned vehicles may capture data in a synchronized fashion to add depth to the image capture and/or a three dimensional and 4 dimensional (over time) aspect to the captured data. In some implementations, UAV position will be contained within a perimeter and the perimeter will have multiple reference points to help each UAV (or other unmanned vehicle) determine a position in relation to static features of a building within which it is operating and also in relation to other unmanned vehicles. Still other aspects include unmanned vehicles that may not only capture data but also function to perform a task, such as paint a wall, drill a hole, cut along a defined path, or other function. As stated throughout this disclosure, the captured data may be incorporated into the virtual model of a Processing Facility.

In another aspect, captured data may be compared to a library of stored data using image recognition software to ascertain and/or affirm a specific location, elevation and direction of an image capture location and proper alignment with the virtual model. Still other aspects may include the use of a compass incorporated into a Smart Device.

In still other implementations, a line of sight from a Smart Device, whether user operated or deployed in an unmanned vehicle, may be used to align the Smart Device with physical reference markers and thereby determine an XY position as well as a Z position. Electronic altitude measurement may also be used in place of, or to supplement, a known altitude of a nearby reference point. This may be particularly useful in the case of availability of only a single reference point.

Reference points may be coded via identifiers, such as a UUID (Universally Unique Identifier), or other identification vehicle. Visual identifiers may include a bar code, hash tag, Alphanumeric or other symbol. Three dimensional markers may also be utilized.

By way of non-limiting example, on site data capture may include designation of an XYZ reference position and one or more of: image capture; infra-red capture; Temperature; Humidity; Airflow; Pressure/tension; Electromagnetic reading; Radiation reading; Sound readings (i.e. level of noise, sound pattern to ascertain equipment running and/or state of disrepair), and other vibration or Sensor readings (such as an accelerometer or transducer).

In some embodiments, vibration data may be used to profile use of the building and/or equipment and machinery associated with the building. For example, vibration detection may be used to determine a machine operation, including automated determination between proper operation of a piece of equipment and/or machinery and faulty operation of the equipment and/or machinery. Accelerometers may first quantify facility operations and production speed and/or capacity during operations. Accelerometers may also detect less than optimal performance of equipment and/or machinery. In some embodiments. AI may be used to analyze and predict proper operation and/or equipment/machinery failure based upon input factors, including vibration patterns captured. Vibrations may include a “signature” based upon machine type and location within a structure human related activity, such as, by way of non-limiting example: machine and foot traffic, physical activities, machine operations, machine failure, raised voices, alarms and alerts, loud music, running, dancing and the like, as well as a number of machines and/or people in the building and a calculated weight and mobility of the people.

Vibration readings may also be used to quantify operation of machinery and equipment associated with the building, such as HVAC, circulators and water pumps. Vibration data may be analyzed to generate profiles for properly running equipment and equipment that may be faulty and/or failing. The improved virtual model of the present invention embodied as an AVM may be updated, either periodically or on one off occasions, such as during a service call or update call.

In some embodiments, a fourth dimension in addition to an XYZ dimension will include date and time and allow for an historical view of a life of a structure to be presented in the virtual model. Accordingly, in some embodiments, onsite cameras and/or Sensors may be deployed and data may be gathered from the on-site cameras and Sensors either periodically or upon command. Data gathered may be incorporated into the improved virtual model.

In still another aspect, the AVM may aggregate data across multiple Commercial Properties and buildings. The aggregated data may include conditions experienced by various buildings and mined or otherwise analyzed, such as via artificial intelligence and unstructured queries. Accordingly, the AVM may quantify reasons relating to one or more of: how to reposition machines, route workflow or otherwise improve, designs that work well; designs that fail; popular aspects; generate multiple Virtual Models with various quantified features; original and modified model versions and almost any combination thereof.

Although data may be gathered in various disparate and/or related ways, an aggregate of data may be quickly and readily accessed via the creation of indexes. Accordingly, indexes may be according to one or more of: date/time stamp; feature; popularity; cost; User specific query; Plumbing; Electrical; HVAC; Structural aspects; Access areas; Periodic data and position capture with camera/Sensor attached to a fixed position; during construction; during modification; during Deployment; airflow; HVAC; machinery; traffic flows during use of structure; audible measurements for noise levels; and almost any other aspect of captured data.

In another aspect, an Augmented Virtual Model may receive data descriptive of generally static information, such as, one or more of: product specifications, building material specifications, product manuals, and maintenance documentation.

Generally static information may be utilized within the Augmented Virtual Model to calculate Performance of various aspects of a Commercial Property. Dynamic data that is captured during one of: a) design data; b) build data; and c) deployed data, may be used to analyze actual Performance of a Commercial Property and also used to update an Augmented Virtual Model and increase the accuracy of additional predictions generated by the Augmented Virtual Model. Maintenance records and supporting documentation may also be archived and accessed via the AVM. A variety of Sensors may monitor conditions associated with one or both of the structure and the parcel. The Sensors and generated data may be used to extrapolate Performance expectations of various components included in the Augmented Virtual Model. Sensor data may also be aggregated with Sensor data from multiple Augmented Virtual Model models from multiple structures and/or Commercial Properties and analyzed in order to track and/or predict Performance of a structure or model going forward.

Glossary

“Ambient Data” as used herein refers to data and data streams captured in an environment proximate to a Vantage Point and/or an equipment item that are not audio data or video data. Examples of Ambient Data include, but are not limited to Sensor perception of: temperature, humidity, particulate, chemical presence, gas presence, light, electromagnetic radiation, electrical power, moisture and mineral presence.

“Analog Sensor” and “Digital Sensor” as used herein include a Sensor operative to quantify a state in the physical world in an analog representation.

“As Built” as used herein refers to details of a physical structure associated with a specific location within the physical structure or parcel and empirical data captured in relation to the specific location.

“As Built Features” as used herein refers to a feature in a virtual model or AVM that is based at least in part upon empirical data captured at or proximate to a correlating physical location of the feature. Examples of As Built Features include placement of structural components such as a wall, doorway, window, plumbing, electrical utility, machinery and/or improvements to a parcel, such as a well, septic, electric or water utility line, easement, berm, pond, wet land, retaining wall, driveway, right of way and the like.

“As Built Imagery” (Image Data) as used herein shall mean image data generated based upon a physical aspect.

“Augmented Virtual Model” (sometimes referred to herein as “AVM”): as used herein is a digital representation of a real property parcel including one or more three dimensional representations of physical structures suitable for commercial use and As Built data captured descriptive of the real property parcel. An Augmented Virtual Model includes As Built Features of the structure and may include improvements and features contained within a Processing Facility.

“Commercial Property” as used herein shall mean one or more real estate parcels suitable for a deployed Processing Facility that may be modeled in an AVM.”

“Directional Indicator” as used herein shall mean a quantification of a direction generated via one or both of: analogue and digital indications.

“Directional Image Data” as used herein refers to image data captured from a Vantage Point with reference to a direction. Image data may include video data.

“Directional Audio” as used herein refers to audio data captured from a Vantage Point within or proximate to a Commercial Property and from a direction.

“Deployment” as used herein shall mean the placement of one or more of: a facility machinery and an equipment item into operation.

“Deployment Performance” as used herein shall mean one or both of: objective and subjective quantification of how one or more of: facility, machinery and an equipment item operated, which may be depicted in an AVM.

“Design Feature” as used herein, shall mean a value for a variable descriptive of a specific portion of a Commercial Property. A Design Feature may include, for example, a size and shape of a structural element or other aspect, such as a doorway, window or beam; a material to be used, an electrical service, a plumbing aspect, a data service, placement of electrical and data outlets; a distance, a length, a number of steps; an incline; or other discernable value for a variable associated with a structure or Commercial Property feature.

“Digital Sensor” as used herein includes a Sensor operative to quantify a state in the physical world in a digital representation.

“Experiential Data” as used herein shall mean data captured on or proximate to a subject Processing Facility descriptive of a condition realized by the Processing Facility. Experiential data is generated by one or more of: digital and/or analog sensors, transducers, image capture devices, microphones, accelerometers, compasses and the like.

“Experiential Sensor Reading” as used herein shall mean a value of a sensor output generated within or proximate to a subject Processing Facility descriptive of a condition realized by the Processing Facility. An Experiential Sensor Reading may be generated by one or more of: digital and/or analog sensors, transducers, image capture devices, microphones, accelerometers, compasses and the like.

“Image Capture Device” or “Scanner” as used herein refers to apparatus for capturing digital or analog image data, an Image capture device may be one or both of: a two dimensional camera (sometimes referred to as “2D”) or a three dimensional camera (sometimes referred to as “3D”). In some examples an Image Capture Device includes a charged coupled device (“CCD”) camera.

“Lag Benefit” as used herein shall mean a benefit derived from, or in relation to a Lead Action.

“Lead Actions” as used herein shall mean an action performed on, in, or in relation to a Commercial Property to facilitate attachment of an Performance Level.

“Performance” as used herein may include a metric of an action or quantity. Examples of Performance may include metrics of: number of processes completed, energy efficiency; length of service; cost of operation; quantity of goods processed or manufacture; quality of goods processed or manufacture; yield; and human resources required.

“Performance Level” as used herein shall mean one or both of a quantity of actions executed and a quality of actions.

“Processing Facility” as used herein shall mean a structure “Quality Level” capable of receiving in a processing material and/or a consumable and outputting a commercial product.

“Sensor” as used herein refers to one or more of a solid state, electro-mechanical, and mechanical device capable of transducing a physical condition or property into an analogue or digital representation and/or metric.

“Smart Device” as used herein includes an electronic device including, or in logical communication with, a processor and digital storage and capable of executing logical commands.

“Total Resources” as used herein shall mean an aggregate of one or more types of resources expended over a time period.

“Vantage Point” as used herein refers to a specified location which may be an actual location within a physical facility or a virtual representation of the actual location within a physical facility.

“Virtual Processing Facility” (“VPS”): as used herein shall mean a digital representation of a physical structure suitable for commercial use. The Virtual Processing Facility may include Design Features and As Built Features. The Virtual Processing Facility may be included as part of an AVM.

Referring now to FIG. 1A a block diagram illustrates various aspects of the present invention and interactions between the respective aspects. The present invention includes an Augmented Virtual Model 111 of a Processing Facility that includes As Built Features. The generation and inclusion of As Built Features, based upon location and direction specific data capture, is discussed more fully below. Data may be transmitted and received via one or both of digital and analog communications, such as via a wireless communication medium 117.

According to the present invention, one or more Deployment Performance Metric 112 are entered into automated apparatus in logical communication with the AVM 111. The Deployment Performance Metric 112 may essentially include a purpose to be achieved during Deployment of a modeled Processing Facility. By way of non-limiting example, a Deployment Performance Level may include one or more of: a production or quantity; quality; yield; scalability; a level of energy efficiency; a level of water consumption; mean time between failure for equipment included in the Processing Facility; mean time between failure for machinery installed in the structure; a threshold period of time between repairs on the Processing Facility; a threshold period of time between upgrades of the Processing Facility; a target market value for a Commercial Property; a target lease or rental value for a Commercial Property; a cost of financing for a Commercial Property; Total Cost of ownership of a Commercial Property; Total Cost of Deployment of a Commercial Property or other quantifiable aspect.

In some embodiments, Deployment Performance Metrics may be related to a fungible item, such as a measurement of energy (KWH of electricity, gallon of fuel oil, cubic foot of gas, etc.); man hours of work; trade medium (i.e. currency, bitcoin, stock, security, option etc.); parts of manufactures volume of material processed or other quantity. Relating multiple disparate Deployment Performance Metrics to a fungible item allows disparate Performance Metrics to be compared for relative value.

Modeled Performance Levels 113 may also be entered into the automated apparatus in logical communication with the AVM 111. The Modeled Performance Levels 113 may include an appropriate level of Performance of an aspect of the structure in the AVM affected by the Deployment Performance Metric 112. For example, a Performance Level 113 for energy efficiency for a structure modeled may include a threshold of KW hours of electricity consumed by the structure on a monthly basis. Similarly, a target market value or lease value may be a threshold pecuniary amount. In some embodiments, a pecuniary amount may be according to a period of time, such as monthly, or a term of years.

Empirical Metrics Data 114 may be generated and entered into the automated apparatus on an ongoing basis. The Empirical Metrics Data 114 will relate to one or more of the Deployment Performance Metrics and may be used to determine compliance with a Deployment Performance Level and/or a Performance Levels. Empirical Metrics Data 114 may include, by way of non-limiting example, one or more of: a unit of energy; an unit of water; a number of service calls; a cost of maintenance; a cost of upgrades; equipment details, design details, machinery details, identification of human resources deployed; identification of organizations deployed; number of human resources; demographics of human resources (i.e. age, gender, occupations, employment status, economic status, requiring assistance with basic living necessities; and the like); percentage of time structure is occupied; purpose of occupancy (i.e. primary residence, secondary residence, short term rental, long term lease, etc.); Sensor readings (as discussed more fully below); man hours required for structure repair/maintenance/upgrades; total currency (or other fungible pecuniary amount) expended on behalf of a structure or property.

In addition to Empirical Metrics Data 114, Lead Actions and expected Lag Benefits 115 that may cause an effect on one or both of a Deployment Performance Level 112 and a Performance Level 113, may be entered into the automated apparatus. A Lead Action may include an action expected to raise, maintain or lower an Empirical Metrics Data 114. For example, an action to install water efficient plumbing fixtures may be scheduled in order to improve water consumption metrics. Similar actions may relate to electrically efficient devices, or automatic electric switches being installed; preventive maintenance being performed; structure automation devices being installed and the like. Other Lead Actions may include limiting a demographic of occupants of a structure to a certain demographic, such as senior citizens. An expected benefit may be measured in Lag Benefit measurements, such as those described as Empirical Metrics Data 114, or less tangible benefits, such as occupant satisfaction.

The automated apparatus may also be operative to calculate Future Performance 116 based upon one or more of: AVM 111 Model with As Built Data; Deployment Performance Metrics 112; Modeled Performance Levels 113 and Empirical Metrics Data 114. Future Performance may be calculated in terms of an appropriate unit of measure for the aspect for which Performance is calculated, such as, for example: an energy unit; man hours; mean time between failures and dollar or other currency amount.

Calculation of Future Performance 116 may be particularly useful to calculate Total Resources calculated to be required to support a particular structure, group of structures, properties and/or group of properties over a term of years (“Total Resources Calculated”). Total Resources Calculated may therefore be related to calculations of Future Performance 116 and include, for example, one or more of: energy units; water units; man hours; equipment; machinery and dollars (or other currency or fungible item). In some embodiments, calculations of Future Performance may include a Total Cost of Ownership for a term of years. For example, a Total Cost of Ownership for a Commercial Property may include a purchase amount and amounts required for maintenance, repair and upgrades from day one of Deployment through twenty years of Deployment (a shorter or longer term of years may also be calculated).

Accordingly, some embodiments may include a calculation of Total Resources required that includes a purchase price of a property with a Processing Facility, that incorporates a total cost associated with the property over a specified term of years. The total cost will be based upon the AVM 111 with As Built Data; Deployment Performance Metrics 112; Modeled Performance Levels 113 and Empirical Metrics Data 114.

Moreover, Total Resources required may be aggregated across multiple properties and. Structures. Aggregation of properties may be organized into property pools to mitigate risk of anomalies in the Calculation of Future Performance. Of course the benefits of property ownership and/or management may also be pooled and compared to the Total Resources required. In various embodiments, different aspects of calculated Future Performance 116 may be aggregated and allocated to disparate parties. For example, first aggregation may relate to man hours of technician time for structure repair and maintenance and the fulfillment of obligations related to the aggregation may be allocated to a first party. A second aggregation may relate to machinery Performance and obligations allocated to a second party. A third aggregation may relate to equipment Performance and obligations allocated to a third party. Other aggregations may similarly be allocated to various parties. In some embodiments, financial obligations incorporating one or both of acquisition cost and ongoing Deployment costs may be allocated and financed as a single loan. Other embodiments include a calculated Future Performance cost being incorporated into a purchase price.

An important aspect of the present invention includes definition and execution of Lead Actions based upon one or more of: the AVM 111 Model with As Built Data; Deployment Performance Metrics 112; Modeled Performance Levels 113; Empirical Metrics Data 114 and Calculations of Future Performance 116.

Referring now to FIG. 1B, an AVM is generally associated with a Commercial Property that includes real estate parcels 140-143. According to some embodiments, one or more of an improvement, a repair, maintenance and an upgrade are performed on the Commercial Property. The Commercial Property is identified according to an automated determination of a location and a particular position, elevation and direction are further determined automatically within the Commercial Property. Smart Devices may be used to access data records stored in an AVM according to a unique identifier of a physical location of the real estate parcels 140-143.

As illustrated, a map of real estate parcels 140-143 is shown with icons 140A-142A indicating parcels 140-142 that have virtual structures 140A-142A included in a virtual model associated with the parcels. Other parcels 143 have an indicator 143A indicating that a virtual model is in process of completion.

In some methods utilized by the present invention, data in an AVM may be accessed via increasingly more accurate determinations. A first level of geospatial location determinations may be based upon the real estate parcels 140-143 themselves and a second geospatial determination may be made according to position locators (discussed more fully below) included within the boundaries of the real estate parcels 140-143. Still more accurate location position may be calculated according to one or both of a direction determination and an accelerometer. Accordingly, it is within the scope of the present invention to access a record of a design model for a specific wall portion within a structure based upon identification of a particular parcel of real estate parcels 140-143 and a location within a structure situated within the real estate parcels 140-143 and height and direction. Likewise, the present invention provides for accessing As Built data and the ability to submit As Built data for a specific portion of a structure based upon an accurate position and direction determination.

In some implementations of the present invention, a Commercial Property unique identifier may be assigned by the AVM and adhere to a standard for universally unique identifiers (UUID), other unique identifiers may be adopted from, or be based upon, an acknowledged standard or value. For example, in some embodiments, a unique identifier may be based upon Cartesian Coordinates, such as global positioning system (GPS) coordinates. Other embodiments may identify a Commercial Property according to one or both of: a street address and a tax map number assigned by a county government of other authority.

In some embodiments, an AVM may also be associated with a larger group of properties, such as a manufacturing plant, research and development, assembly, a complex, or other defined arrangement.

As illustrated, in some preferred embodiments, an electronic record correlating with a specific Commercial Property may be identified and then accessed based upon coordinates generated by a GPS device, or other electronic location device. The GPS device may determine a location and correlate the determined location with an AVM record listing model data, As Built data, improvement data, Performance data, maintenance data, cost of operation data, return on investment data and the like.

Referring now to FIG. 1C, a relational view of an Augmented Virtual Model 100 with a Virtual Processing Facility 102B is illustrated. The Augmented Virtual Model 100 includes a virtual model stored in digital form with a design aspect that allows for a physical structure 102A suitable for commercial use to be designed and modelled in a virtual environment. The design aspect may reference Performance data of features to be included in a Virtual Processing Facility 102B and also reference variables quantifying an intended use of the Virtual Processing Facility 102B. The Virtual Processing Facility 102B and the Augmented Virtual Model 100 may reside in a virtual setting via appropriate automated apparatus 108. The automated apparatus 108 will typically include one or more computer servers and automated processors as described more fully below and may be accessible via known networking protocols. The Physical Structure 102A may include transceivers 120 or other type of sensor or transmitter or receivers that monitor an area of ingress and egress 122, such as a doorway, elevator and/or loading dock. Reference point transceivers 121A may be used as wireless references of a geospatial position. A wireless communication device 123 may also link logical infrastructure within the structure 102A with a digital communications network.

In correlation with the design aspect, the present invention includes an As Built Model 101 that generates a Virtual Processing Facility 102A in the context of the Augmented Virtual Model 100. The As Built Model 101 includes virtual details based upon As Built data captured on or proximate to a physical situs of a related commercial physical structure 102A. The As Built data may be captured, for example, during construction or modification of a physical structure 102A.

The As Built Model 101 may include detailed data including image captures via one or more image capture devices 107 and physical measurements of features included in the physical structure 102A. The physical measurements may be during a build phase of the physical structure; or subsequent to the build phase of the physical structure. In some embodiments, original As Built measurements may be supplemented with additional data structure data associated with repairs or improvements are made to the physical structure. Details of recordable build aspects are placed as digital data on a recordable medium 104 included in the automated apparatus 108. Communications may occur between the automated apparatus 108 and users at a site via wireless communications devices 123.

The digital data included on a recordable medium 104 may therefore include, for example, one or more of: physical measurements capturing Experiential Data; image data (i.e. digital photos captured with a CCD device); laser scans; infra-red scans and other measurement mediums. One or more records on the recordable medium 104 of an As Built structure may be incorporated into the Augmented Virtual Model 100 thereby maintaining the parallel nature of the Augmented Virtual Model 100 with the physical structure 102A.

In some embodiments, As Built data on a recordable medium 104 may be generated and/or captured via an image capture device within device 119.

As the physical structure is deployed for use, subsequent measurements that generate and/or capture Experiential Data may be made and incorporated into the Augmented Virtual Model 100. In addition, a user may access and update 103 the Augmented Virtual Model 100 to ascertain features of the physical structure 102A that have been virtually incorporated into the Augmented Virtual Model 100. In some examples, a tablet, handheld network access device (such as, for example a mobile phone) or other device with automated location service may be used to determine a general location of a physical structure 102A. For example, a smart phone with global positioning system (GPS) capabilities may be used to determine a physical address of a physical structure, such as 123 Main Street. Stored records containing data relating to 123 Main Street may be accessed via the Internet or other distributed network.

In addition to the use of GPS to determine a location of a User Device, the present invention provides for a real estate parcel with a physical structure 102A that includes more radio frequency (or other mechanism) location identifiers 121A. Location identifiers 121A may include, for example, radio transmitters at a defined location that may be used to accurately identify via triangulation, a position of a user device 106, such as a: tablet, smart phone or virtual reality device. The position may be determined via triangulation, single strength, time delay determination or other process. In some embodiments, triangulation may determine a location of a user device within millimeters of accuracy.

Other location identifiers may include, by way of non-limiting example, RFID chips, a visual markings (i.e. a hash tags or barcode), pins or other accurately placed indicators. Placement of the location identifiers may be included in the AVM and referenced as the location of the physical user device is determined. As described above, specific location identifiers may be referenced in the context of GPS coordinates or other more general location identifiers.

Based upon the calculated location of the user device 106, details of the physical structure 102A may be incorporated into the Virtual Processing Facility 102B and presented to a user via a graphical user interface (GUI) on the user device 106.

For example, a user may approach a physical structure and activate an app on a mobile user device 106. The app may cause the user device 106 to activate a GPS circuit included in the user device and determine a general location of the user device 106, such as a street address designation. The general location will allow a correct AVM to be accessed via a distributed network, such as the Internet. Once accessed, the app may additionally search for one or more location identifiers 121A of a type and in a location recorded in the AVM. An AVM may indicate that one or more RFID chips are accessible in a kitchen, a living room and each bedroom of a structure. The user may activate appropriate Sensors to read the RFID chips and determine their location. In another aspect, an Augmented Virtual Model 100 may indicate that location identifiers 121A are placed at two or more corners (or other placement) of a physical structure 102A and each of the location identifiers 121A may include a transmitter with a defined location and at a defined height. The user device 106, or other type of controller, may then triangulate with the location identifiers 121A to calculate a precise location and height within the physical structure.

Similarly, a direction may be calculated via a prescribed movement of the user device 106 during execution of code that will record a change in position relative to the location identifiers 121A. For example, a user smart device, such as a smart phone or user device 106 may be directed towards a wall or other structure portion and upon execution of executable code, the smart device may be moved in a generally tangential direction towards the wall. The change in direction of the user device 106 relative to the location identifiers 121A may be used to calculate a direction. Based upon a recorded position within the structure 102A and the calculated direction, a data record may be accessed in the Augmented Virtual Model 100 and a specific portion of the Augmented Virtual Model 100 and/or the Virtual Processing Facility 102B may be presented on the user device 106. In other embodiments, a direction may be made, or verified via a mechanism internal to the smart device, such as a compass or accelerometer.

In still another aspect of the present invention, in some embodiments, transmissions from one or more location identifiers 121A may be controlled via one or more of: encryption; encoding; password protection; private/public key synchronization or other signal access restriction. Control of access to location identifiers 121A may be useful in multiple respects, for example, a location identifier may additionally function to provide access to data, a distributed network and/or the Internet.

The Virtual Processing Facility 102B may include one or both of: historical data and most current data relating to aspects viewable or proximate to the user device 106 while the user device is at the calculated location in the physical structure 102A. In this way, the parallel virtual world of the Augmented Virtual Model 100 and the Virtual Processing Facility 102B may present data from the virtual world that emulates aspects in the physical world, and may be useful to the user accessing the user device 106, while the user device is at a particular physical location. As discussed within this document, data presented via the Augmented Virtual Model 100 may include one or more of: design data, As Built data, Experiential Data, Performance data relating to machinery and/or features of the Augmented Virtual Model 100 or physical structure; maintenance data, and annotations.

Annotations may include, for example, a user's or designer's note recorded at a previous time, a service bulletin, maintenance log, operation instructions or a personal note to a subsequent user, such as a virtual “John Smith was here” such guest log indicating who had frequented the location. Annotations may include one or both of text and image data. For example, an annotation may include an image of the location captured at a given time and date. The image may be of a personal nature, i.e. the living room while the Smith's owned the structure, or a professional nature, i.e. the living room after being painted by XYZ Contractor on a recorded date. In some embodiments, annotations may be used to indicate completion of a work order. Recordation of completion of a work order may in turn trigger a payment mechanism for paying an entity contracted to complete the work order. In another aspect, annotations may relate to an AVM or a Virtual Processing Facility as a whole, or to a particular aspect that is proximate to a location of the user device within the Virtual Processing Facility.

In some embodiments, details of a proposed use of a structure and parcel may be input into a design module and used to specify or recommend features to be included in an Augmented Virtual Model 100.

According to the present invention, features of a Processing Facility and parcel are generated within a digital design model and then tracked as the features are implemented in a build process and further tracked in Performance of the structure as it is placed into use. To the extent available, Performance is tracked in the context of variables relating to use. Variables may include, for example: a use of the structure, such as manufacturing and/or processing; a number of resources accessing in a structure; demographics of the human resources; number of months per year the structure is deployed for use; which months of the year a structure is deployed for use; which hours of the day the structure is occupied and other relevant information.

As Experiential Sensor Readings are generated they may be memorialized to generate Experiential Data associated with a physical structure 102A. The Experiential Data is collected and analyzed via structured queries and may also be analyzed with Artificial Intelligence processes such as unstructured queries to derive value. In some embodiments, Experiential Data may also be associated with a human and/or an animal interacting with the structure 102A. Whereas former process plants were generally designed and built to mitigate against variability in a human 118 and between disparate humans 118. The present invention allows for human variability to be monitored via sensors within device 119 and the structure to be modified to optimally inter-relate with the values for variables attributable to a human 118 that will inhabit or otherwise interact with the structure 102A. Human (and/or animal) maybe quantified with sensors within device 119 installed on or proximate to the Human 118. Alternatively, sensors 124 located in, or proximate to, a structure 102A may be used to monitor human variability. Biosensors may be used to provide empirical data of humans 118 interacting with a structure may be analyzed using structured or unstructured queries to device relationships between structure performance and human biometrics. Accordingly, sensors may be used to quantify interaction between a human 118 and an As Built structure 102A according to physiological and behavioral data, social interactions, and environmental factors within the structure, actions undertaken, movements, and almost any quantifiable aspect.

As Built Features and biometrics may be further utilized to control various structure automation devices. Structure automation devices may include, by way of non-limiting example one or more of: automated locks or other security devices; thermostats, lighting, heating, chemical processing, cutting, molding, laser shaping, 3D printing, assembly, cleaning, packaging and the like. Accordingly, a structure with recorded As Built design features and vibration sensors may track activities in a structure and determine that a first occupant associated with a first vibration pattern of walking is in the structure. Recorded vibration patterns may indicate that person one is walking down a hallway and automatically turn on appropriated lighting and adjust one or more of: temperature, sound and security. Security may include locking doors for which person one is not programmed to access. For example, a first pattern of vibration may be used to automatically ascertain that a person is traversing an area of a structure for which a high level of security is required or an area that is designated for limited access due to safety concerns. As Built data has been collected. Other structure automation may be similarly deployed according to As Built data, occupant profiles, biometric data, time of day, or other combination of available sensor readings.

Referring now to FIG. 1D, according to the present invention a virtual model is generated that correlates with a physical facility 152 and includes virtual representations of As Built features and Experiential Data. As discussed more fully herein, the virtual model may include an AVM with As Built data, such as image data and measurements, included within the model. In addition, sensor data may be collected over time and incorporated into the AVM. The AVM may include virtual representations of one or more of: sensors 155; equipment 156-158; controls 161; infrastructure 159, such as HVAC, utilities, such as electric and water, gas lines, data lines, etc. and vantage points 151.

In some implementations, a virtual reality headset may be worn by a user to provide an immersive experience from a vantage point 151 such that the user will experience a virtual representation of what it would be like to be located at the vantage point 151 within the facility 152 at a specified point in time. The virtual representation may include a combination of Design Features, As Built Data and Experiential Data. A virtual representation may therefore include a virtual representation of image data via the visual light spectrum, image data via infrared light spectrum, noise and vibration reenactment. Although some specific types of exemplary sensor data have been described, the descriptions are not meant to be limiting unless specifically claimed as a limitation and it is within the scope of this invention to include a virtual representation based upon other types of captured sensor data may also be included in the AVM virtual reality representation.

Referring now to FIG. 1E, a user 131 is illustrated situated within an AVM 111. The user 131 will be virtually located at a Vantage Point 137 and may receive data 136, including, but not limited to one or more of: image data 134, audio data 135 and Ambient Data 136. The user 131 may also be provided with controls 133. Controls 133 may include, for example, zoom, volume, scroll of data fields and selection of data fields. Controls may be operated based upon an item of Equipment 132 within a Field of View 138 of the User 131 located at a vantage point 137 and viewing a selected direction (Z axis). The user is presented with Image Data from within the AVM 111 that includes As Built data and virtual design data.

Additional examples may include sensor arrays, audio capture arrays and camera arrays with multiple data collection angles that may be complete 360 degree camera arrays or directional arrays, for example, in some examples, a sensor array (including image capture sensors) may include at least 120 degrees of data capture, additional examples include a sensor array with at least 180 degrees of image capture; and still other examples include a sensor array with at least 270 degrees of image capture. In various examples, data capture may include sensors arranged to capture image data in directions that are planar or oblique in relation to one another.

Referring now to FIG. 2, a functional block illustrates various components of some implementations of the present invention. According to the present invention automated apparatus included in the AVM 201 are used to generate a model of a Virtual Processing Facility (“VPS”) and may also incorporate a model and associated real estate parcel (“VPS”). One or more pieces of equipment that will be deployed in the Commercial Property may be included into the augmented virtual model 201, equipment may include, for example: machinery 222; building support items 212, and utilities support 213. The AVM 201 may model operational expectations 204 during deployment of a facility and associated machinery and equipment included in the AVM 201. Machinery 211 may include, for example, manufacturing tools, robots or other automation, transport tools, chemical processing machine, physical processing machine, assembly machine, heat processing machine, cooling machine, deposition device, etching device, welding apparatus, cutting apparatus, forming tool, drilling tool, shaping tool, transport machine, structure automation, air purification or filter systems, noise containment device and the like. Utility support equipment may include cabling, dish antennas, Wi-Fi, water softener, water filter, power, chemical supply, gas supply, compressed air supply and the like, as well as uptime and downtime associated with a facility utility 243.

The AVM 201 calculates a predicted Performance of the AVM and generates Operational Levels 204 based upon the Performance 222, wherein “Performance” may include one or more of: total cost of deployment 214; operational experience 203 which may include one or both of: objective empirical measurements and satisfaction of operator's use an As Built physical model based upon the AVM; operational expectations 204, total maintenance cost 206, and residual value of an As Built following a term of years of occupation and use of an As Built Facility based upon the AVM. Performance 221 may also be associated with a specific item of machinery 211.

In another aspect, actual Operational Experience 203 may be monitored, quantified and recorded by the AVM 201. Data quantifying the Operational Experience 203 may be collected, by way of non-limiting example, from one or more of: Sensors incorporated into an As Built structure; maintenance records; utility records indicating an amount of energy 202 (electricity, gas, heating oil) consumed; water usage; periodic measurements of an As Built structure, such as an infra-red scan of climate containment, air flow through air handlers, water flow, water quality and the like; user surveys and maintenance and replacement records.

In still another aspect, a warranty 205 covering one or both of parts and labor associated with an As Built structure may be tracked, including replacement materials 207. The warranty 205 may apply to an actual structure, or one or more of machinery 211; building support item 212; and utility support item 213.

The AVM 201 may take into account a proposed usage of a Deployment of a Processing Facility based upon values for Deployment variables, and specify aspects of one or more of: Machine s 211; building support 212; and utility support 213 based upon one or both of a proposed usage and values for Deployment variables. Proposed usage may include, for example, how many human resources will occupy a Processing Facility, demographics of the resources that will occupy the Processing Facility; percentage of time that the Processing Facility will be occupied, whether the Processing Facility is a primary residence, whether the Processing Facility is a leased property and typical duration of leases entered into, environmental conditions experienced by the Processing Facility, such as exposure to ocean salt, Winter conditions, desert conditions, high winds, heavy rain, high humidity, or other weather conditions.

In another aspect, Deployment may relate to biometrics or other data associated with specific occupants of a structure. Accordingly, in some embodiments, sensors may monitor biologically related variables of occupants and/or proposed occupants. The biometric measurements may be used to determine one or both of Lead Actions and Lag Metrics. Lead actions may include one or more of: use of specific building materials, selection of design aspects; Deployment of structure equipment; Deployment of machinery; terms of a lease; length of a lease: terms of a maintenance contract; and structure automation controls.

According to the present invention, design aspects and structure materials 210 may also be based upon the proposed usage and values for Deployment variables. For example, a thicker exterior wall with higher insulation value may be based upon a structures location in an adverse environment. Accordingly, various demographic considerations and proposed usage of a structure may be used as input in specifying almost any aspect of a Processing Facility.

Total Cost of Deployment (TCD)

In still another consideration, a monetary value for one or more of: a Total Cost of Deployment (“TCD”). Total maintenance cost (“TMC”) and a desired return on investment (“ROI”) for a Commercial Property may be used as input for one or more design aspects included in an Augmented Virtual Model System 200. Total Cost of Ownership, Total Maintenance Cost and ROI may be used to determine optimal values of variables 202-205, 210-213 specified in an Augmented Virtual Model System 200 and incorporated into an As Built structure, and other improvements to a real estate parcel.

A Total Cost of Deployment 214 may change based upon a time period 215 used to assess the Total Cost of Deployment 214. A ROI may include one or more of: a rental value that may produce a revenue stream, a resale value, a cost of operation, real estate taxes based upon structure specifications and almost any other factor that relates to one or both of a cost and value.

Desirable efficiency and Performance may be calculated according to one or more of: established metrics, measurement protocols and past experience. The AVM 201 and associated technology and software may be used to support a determination of a TCD. In another aspect, a TCD may be based upon an assembly of multiple individual metrics, procedures to assess metrics, procedures to adjust and optimize metrics and procedures to apply best results from benchmark operations. In the course of managing Total Cost of Ownership, in some examples, initial steps may include design aspects that model an optimal design based upon Total Cost of Ownership metrics and also model designed algorithms used to assess Total Cost of Ownership metrics.

In the following examples, various aspects of Total Cost of Deployment 214, Total Maintenance Costs, and associated metrics, are considered in the context of calculating a target Total Cost of Deployment 214. Accordingly, the AVM may be used to TCD optimization.

A designed Processing Facility is ultimately built at a site on a real estate parcel. A build process may be specified and provide metrics that may be used in a process designed by a AVM 201 and also used as a physical build proceeds. In some examples, time factors associated with a physical build may be important, and in some examples time factors associated with a physical build may be estimated, measured and acted upon as they are generated in a physical build process. Examples of time factors may include, one or more of: a time to develop and approve site plans; a time to prepare the site and locate community provided utilities or site provided utilities; a time to lay foundations; a time to build structure; a time to finish structure; a time to install internal utilities and facilities related aspects; a time to install, debug, qualify and release equipment; times to start production runs and to certify compliance of production are all examples of times that can be measured by various techniques and sensing equipment on a Processing Facility 's site. Various time factors for a build are valuable and may become increasingly valuable as a physical build proceeds since the monetary investment in the project builds before revenue flows and monetary investments have clearly defined cost of capital aspects that scale with the time value of money.

Various build steps may include material flows of various types. Material flow aspects may be tracked and controlled for cost and efficiency. Various materials may lower a build materials cost, but raise time factors to complete the build. Logical variations may be calculated and assessed in an AVM 201 and optimal build steps may be generated and/or selected based upon a significance placed upon various benefits and consequences of a given variable value. Physical build measurements and/or sensing on physical build projects may also be used as input in an assessment of economic trade-offs.

The equipment deployed may incur a majority of a build cost depending upon user defined target values. The AVM may model and present alternatives including one or more of: cost versus efficiency, quality 240, time to build, life expectancy, market valuation over time. A cost to build may be correlated with cost to deploy and eventual resale. An overall model of a Total Cost of Deployment 214 may include any or all such aspects and may also include external. In some examples, the nature of equipment trade-offs may be static and estimations may be made from previous results. In some other examples, changes in technology, strategic changes in sourcing, times of acquisition and the like may play into models of Total Cost of Deployment 214.

In some examples, an initial efficiency of design which incurs large costs at early stages of a project may have a dominant impact on Total Cost of Deployment 214 when time factors are weighted to real costs. In other examples, the ability of a Processing Facility to be flexible over time and to be changed in such flexible manners, where such changes are efficiently designed may dominate even if the initial cost aspects may be less efficient due to the need to design in flexibility. As a Processing Facility is built, and as it is operated the nature of changing customer needs may create dynamic aspects to estimations of Total Cost of Deployment 214. Therefore, in some examples, estimates on the expected dynamic nature of demands on a Processing Facility may be modeled against the cost aspects of flexibility to model expectations of Total Cost of Deployment 214 given a level of change.

In some examples, factors that may be less dependent on extrinsic factors, such as product demand and the like may still be important metrics in Total Cost of Deployment 214. Included in the As Built factors may be calculations such as HVAC temperature load, in which personnel and seasonal weather implications may be important. AVM models may include a user interface to receive value useful in the AVM models. In addition, electronic monitoring, via Sensors that may determine energy consumption, includes for example: electricity, fuel oil, natural gas, propane and the like may be useful for estimation and measurement.

Temperatures may be monitored by thermocouples, semiconductor junction based devices or other such direct measurement techniques. In other examples, temperature and heat flows may be estimated based on photon based measurement, such as surveying the Processing Facility with infra-red imaging or the like.

Utility load may be monitored on a Processing Facility wide basis and/or at point of use monitoring equipment located at hubs or individual pieces of equipment itself. Flow meters may be inline, or external to pipes wires or conduits. Gases and liquid flows may be measured with physical flow measurements or sound based measurement. In other examples, electricity may be monitored as direct current measurements or inferred inductive current measurement.

In some examples, the nature and design of standard usage patterns of a Processing Facility and an associated environment may have relevance to Total Cost of Ownership. For example, usage that includes a larger number of ingress and egress will expose an HVAC system to increased load and usage that includes a significant number of waking hours with inhabitants in the commercial building may incur increased usage of one or more of: machinery 211; building support devices 212; and utilities 234.

The nature and measurement aspects of vibration in the Processing Facility may also be modelled and designed as the Processing Facility is built. There may be numerous means to measure vibrations from capacitive and resistive based measurements to optical based measurements that measure a subtle change in distance scale as a means of detecting vibration. Vibration may result from a Processing Facility being located proximate to a roadway, train, subway, airport, tidal flow or other significant source of relatively consistent vibration. Vibration may also be more periodic, such as earthquake activity. In still another aspect, vibration may result from human traffic within the Commercial Property. The use of vibration monitoring Sensors may indicate various activities that take place within the structure and facilitate more accurate modeling of a life expectancy of various aspects of the structure as well as machines located within the structure.

Noise levels are another type of vibrational measurement which is focused on transmission through the atmosphere of the Processing Facility. In some cases, noise may emanate from one location after moving through solid structure from its true source at another location. Thus, measurement of ambient sound with directional microphones or other microphonic sensing types may be used to elucidate the nature and location of noise emanations. In some cases, other study of the noise emanations may lead to establishment of vibrational measurement of different sources of noise. Floors, ceilings, doorways, countertops, windows and other aspects of a Processing Facility may be monitored in order to quantify and extrapolate noise levels. Noise and vibrational measurement devices may be global and monitor a region of a Processing Facility, or they may be inherently incorporated into or upon individual equipment of the Processing Facility.

In some examples, models of a Processing Facility (including original models and As Built models) may include routings of pipes, wires, conduits and other features of a Processing Facility and the installed equipment that have structure. Together with models of the building structure and the equipment placed in the building the various routed structures may be married in a detailed AVM 201.

In another aspect, an AVM 201 may include conflicts between the physical structures may be detected and avoided in the design stage at far improved cost aspects. In some examples, a designer may virtually ascertain a nature of the conflict and alter a design in virtual space to optimize operational aspects. Additionally, in some embodiments, an As Built model may be generated during and after a Processing Facility is built for various purposes. In some examples, a technician may inspect a Processing Facility for conformance of the build to the designed model. In other examples, as an As Built Processing Facility is altered to deal with needed changes, changes will be captured and included in the As Built AVM 201.

In another aspect of the present invention, the AVM 201 may be used to generate a virtual reality model of a Commercial Property, including one or more structures that may be displayed via user interface that includes an immersion of the user into a virtual setting. Immersion may be accomplished, for example, via use of a virtual reality headset with visual input other than a display screen is limited. In some embodiments, a virtual setting may be generated based upon a location of the user. For example, GPS coordinates may indicate a Commercial Property and a user may wear a headset that immerses the user in a virtual reality setting. The virtual reality setting may display one or more virtual models of structures that may be potentially constructed on the Commercial Property.

Embodiments may include models generated, standard modelling software such as BIM 360™ field which may support the display of a Processing Facility design in a very complete level of detail. Modelling of a Processing Facility in its location or proposed location, or in multiple proposed locations, may be useful from a Total Cost of Ownership perspective, especially from an evaluation of the nature of a site layout including real estate property parcel options and the like.

In some examples, a virtual display observed in the field at the site of an As Built or proposed build may allow for design changes and design evaluations to be viewed in a space before build is completed. For example, a structure may be completed to the extent that walls, floors and ceilings are in place. A user may utilize a virtual display to understand the layout difference for different designs and the designs may be iterated from designs with the least flexibility to more flexible yet more complex designs.

In some examples, the design systems may include various types of features such as building structure, walls, ducts, utilities, pipes, lighting, and electrical equipment. The design systems are augmented with As Built Data and Experiential Data.

The design and modelling systems may be utilized to simulate and project cost spending profiles and budgeting aspects. The modelling systems may therefore be useful during the course of an audit, particularly when comparing actual versus projected spending profiles. The comparison of various spend sequencing may be used to optimize financing costs, maintenance, refurbishing and sequencing. The AVM 201 may be useful to provide early estimates, and for cost tracking versus projections which may be visualized as displays across a virtual display of the building, facilities and equipment.

Energy/Utilities Cost: There may be numerous examples of tradeoffs in sources of electric energy to a Processing Facility. For example, a site may be designed with various utility supplies for power, with tailored power management systems to balance the capacitance and impedance of the effective load to minimize electricity cost. In addition, various alternative forms of electric energy may be assessed and designed. Solar, geothermal and Wind generated electric power may make economic sense under certain conditions and may have time of day and seasonal relevance. The design of flexible support facilities for the installation of initial energy generation capacity with provision for the addition of additional capacity may be assessed. In some instances, backup power generation may be designed to ensure that a Processing Facility may run at some level for a certain period of time. In some cases, this may allow for continued production, in other examples, backup power may give a Processing Facility the time to idle and shut down capacity in a safer and less damaging manner.

In some examples, an energy source for heating, cooling, humidification and dehumidification equipment may be modelled and managed. In some examples, a source of energy used may be one or more of electric, natural gas, propane, fuel oil or natural gas. Emergency backup may also be modelled and managed. Various choices between electric sources. Solar and fuel based energy consumption may be modelled and controlled based on upon market forecasts. Estimates may be periodically adjusted according to world and/or market events.

Enhanced inspection, and guidance capabilities enabled via ongoing electronic Sensor measurements may facilitate one or more of: maintenance, expansion and optimization of Processing Facility features, operation Commercial Property equipment and maintenance models. Ongoing monitoring via Sensor data collection also increases knowledge of machines and operations, or other useful capacities towards knowing the state of the Processing Facility.

Decisions related to maintenance of equipment and facilities may be important decisions that modelling and operational management systems support. The various cost elements that may go into modelling may include, for example, one or more variables related to consumables, such as: a cost of consumables; frequency of replacement 241, quantity of consumables 242, life of replaced parts, nature of failures of different part types; manpower associated with planned and unplanned maintenance and expected and actual life of equipment

Inside of a functional Processing Facility, augmented reality functions viewable in an AVM 201 including an AVM may be used to guide operators, surveyors, repair workers, or other individuals, through the Processing Facility. As one non-limiting example, a tablet, mobile device, or other small device with a screen, imaging, and other sensing capabilities may be used in an augmented reality fashion towards this function.

As described above, facing a mobile device towards an area in a Processing Facility and movement of the mobile device in a particular pattern may be used to ascertain a specific area of the Processing Facility for which AVM 201 data should be accessed. A combination of one or more of: image, location, orientation, and other Sensors may also be used to identify to the mobile device, which wall segment, building aspect, machinery or equipment the device is identifying. A location of mobile device, a height and an angle of view may also be utilized to determine aspects of the structure for which a virtual model is being requested.

In some embodiments, a user may be presented with various layers of data, including, for example, one or more of: structural aspects of the Processing Facility, plumbing, electrical, data runs, material specifications or other documentation, including but not limited to: basic identifying information, installation information, service records, safety manuals, process records, expected service schedule, among many other possibilities.

A plurality of information may be thus easily accessible inside the Processing Facility, and may be used for a variety of functions, including finding a specific machine to then diagnose and service a problem, regular inspection of equipment, guided tours of the Processing Facility, or many other functions. This information may be conveyed to the individual in a plurality of possible formats, such as lists that show up on the screen, clickable icons that show up next to the equipment in a Virtual Reality (“VR”) camera feed, or many other possibilities. These functions may also be accessible in a hands-free VR format with a VR headset, or other such device.

As the user is inside a Processing Facility, the user may receive a plurality of information, instructions, etc. while the user is proximate to the various aspects of the structures. For example, the user machines themselves, seeing them work, hearing the sounds they make, etc. to better inspect or service, among other possible functions, the Processing Facility 's equipment. With VR systems, similar travel, guidance, or inspection capabilities for a functional Processing Facility may be achieved completely remotely from the Processing Facility itself. Additionally, with VR systems, these capabilities may occur prior, during, or after the construction and deployment of a Processing Facility.

A VR system may constitute a headset or lens system with stereoscopic viewing capabilities, a sound conveying means, such as headphones, and various forms of user input, such as a handheld controller or foot pedals as non-limiting examples. Various forms of imaging, surveying, or modeling technology may be used to generate virtual models of a functional Processing Facility. As a non-limiting example, exploring such a model with a VR system may be used to examine layout, functioning, or other parameters of a Processing Facility before its construction. As an alternative non-limiting example, exploring a model possibly generated by sensing technology in real time, or over a period of time prior to viewing with a VR system, may allow for inspection or demonstration capabilities in a location entirely remotely from the actual Processing Facility itself. This may include both imagery and sounds captured within the Processing Facility.

Collection of data may additionally include actual service life experienced and performance of equipment used in an AVM which thereby enables enhanced modeling of a life expectancy of equipment included in an Augmented Virtual Model 100 and an As Built structure. Various Sensors may gather relevant data related to one or more of: use of machinery and equipment, performance of machinery items of equipment and an ambient environment inside or proximate to machinery and equipment. In addition, an unstructured query relating to the functioning or life expectancy of equipment may be generated by a processor to access and interpret data, thereby deriving relevant input to a decision maker based upon analysis of the data.

Various examples of data to be acquired, relating to life expectancy of equipment, may include, but is not limited to, hours of operation, conditions of operation (whether and how long the equipment may be running under capacity, at rated capacity, or over capacity), or many environmental conditions for operation; environmental conditions may include the ambient temperature (or the difference in ambient temperature from an ideal or other measured value), ambient humidity (or the difference in ambient humidity from an ideal or other measured value), ambient air particulate content (or a comparison of the current air particulate level to a filter change schedule), presence or concentration of ambient gasses (if relevant) such as carbon dioxide, or other gas, a number of times of ingress or egress into the Processing Facility which may change ambient conditions or other trackable data.

Identification of Equipment

Identification capabilities may be facilitated or improved for one or more of: structural aspects, machinery, equipment and utility support within the Processing Facility. This identification may take many forms through various means of query and communication, and may be facilitated through various hardware and/or software means.

Non-limiting examples may include image based identification; a device with some imaging means, including but not limited to a mobile device camera, tablet device camera, computer camera, security camera, or AR headset camera may image the equipment to be identified. Image recognition software may be used to identify the visualized equipment by its identifying features. Machine learning may be used to train systems using this software to identify specific features of the equipment in question. Other types of visual identifiers including but not limited to QR codes, may be used to visually identify equipment.

An additional non-limiting example may include location based identification; a device with some location means, including but not limited to GPS, internal dead-reckoning, or other means, may be used to determine a location within a Processing Facility. Identifying information for equipment at or near the measured location may be accessed for assessment, based on its proximity to the location based signal.

An additional non-limiting example may also include direction based identification; with a fixed location, or in tandem with a location means, a device may have capabilities to deduce orientation based information of the device. This orientation information may be used to deduce a direction that the device is pointing in. This direction based information may be used to indicate that the device is pointing to a specific piece of equipment that may be identified.

An additional non-limiting example may also include As Built sensor and sensor generated experiential data based identification; identifying information for various equipment may be stored and accessed within a database storing this information. This information may be accessed by various means by a user with certain qualification to that information.

An additional non-limiting example may include tag based identification; identifying information for various equipment may be accessed through proximity to many non-limiting examples of tagging capabilities, such as magnetic tags, bar code tags, or others. These tags may contain the information in question, or may reference the location of pertinent information to the owner, in order to convey this information to the owner.

An additional non-limiting example, data aggregation may include sensors generating data that is associated with an IoT (Internet of Things) based identification. Various IoT devices (or Sensors) may include a digital storage, processor and transmitter for storing and conveying identifying information. Upon request, an IoT device may relay identifying information of itself to a human with a communicating device, or to its neighbors. It may also possibly convey information received from and/or sent to other internet connected devices as well.

-   -   Data aggregated and stored for reference in calculation of Cost         of Upkeep considered in a TOC and may include data related to         some or all of:     -   Documented items covered;     -   Long term warranty for Processing Facility/building ownership;     -   Items included in purchase price;     -   financed amounts;     -   Tax implications;     -   Capital value;     -   Ability to expand Processing Facility and/or structural features         such as baths or kitchens;     -   Lateral dimensions;     -   Vertical dimensions;     -   Building support systems;     -   Utilities;     -   Electric;     -   Water;     -   Discharge;     -   Aggregate Data;     -   Same Processing Facility;     -   Multiple similar facilities;     -   Disparate Processing Facility types;     -   Same geographic area;     -   Disparate geographic areas;     -   Locating Machine s and Equipment;     -   GPS (may be used in combination with other location         technologies;     -   Near field communication with reference point emitter in         Processing Facility;     -   Wi-Fi;     -   RFID;     -   Reflector tags;     -   “Visual” recognition identifiers, i.e. hash, barcode; and     -   Directional—accelerometers in combination with visual         recognition identifiers.

As per the above listing, functionality may therefore include modeled and tracked Performance of a Processing Facility and equipment contained within the Processing Facility, including consumables 233 used and timing of receipt and processing of consumables; modeled and actual maintenance 232, including quality of maintenance performed; equipment Performance including yields; Consumables 233 tracking may include a frequency of replacement and quantity of replaced consumables; Utilities 234 tracking may include projected and actually units of energy consumed.

3D Scanning & Model Development

In one aspect of the present invention data related to the position and identity of substantial elements of a Processing Facility are first designed and then recorded in their actual placement and installation. This may include locations of building features, such as beams, walls, electrical junctions, plumbing and etc. as the structure is designed and constructed. As part of the Processing Facility model, laser scanning may be performed on site at various disparate times during construction. An initial scan may provide general information relating to the location of the structure in relationship to elements on the property such as roadways, utilizes such as electricity, water, gas and sewer to identify non-limiting examples.

Additional events for scanning may occur during the construction process in order to capture accurate, three-dimensional (3D) “as-built” point cloud information. Point cloud may include an array of points determined from image capture and/or laser scanning or other data collection technique of As Built features. In some examples, captured data may be converted into a 3D model, and saved within a cloud-based data platform.

In some examples other methods of capturing spatially accurate information may include the use of drones and optical scanning techniques which may include high resolution imagery obtained from multiple viewpoints. Scanning may be performed with light based methods such as a CCD camera. Other methods may include infrared, ultraviolet, acoustic, and magnetic and electric field mapping techniques may be utilized.

Processing Facility related information may include physical features generally associated with an exterior of a structure such as geo-location, elevation, surrounding trees and large landscaping features, underground utility locations (such as power, water, sewer, sprinkler system, and many other possible underground utility features), paving, and pool or patio areas. Processing Facility related information may also include features generally related to a structure such as underground plumbing locations, stud locations, electrical conduit and wiring, vertical plumbing piping, and HVAC systems or other duct work. The acquisition of the data may allow the model system to accurately locate these interior and exterior features. Acquisition of As Built data during different points of the construction completion allows measurements to be taken prior to aspects involved in a measurement process being concealed by concrete, sheetrock or other various building materials.

Data is acquired that is descriptive of actual physical features as the features are built and converted into a 3D model which may be referred to as the “As Built” model. The As Built model will include “key components” of the structure and be provided with a level of artificial intelligence that fully describes the key component. In some embodiments, the As Built model may be compared to a design model. In some implementations “intelligent parameters” are associated with key components within the 3D model. For example, key components and associated information may further be associated with intelligent parameters. Intelligent parameters for the key components may include the manufacturer, model number, features, options, operational parameters, whether or not an option is installed (and if so, its features and dimensions), any hardware associated with the key component (and its manufacturer and serial number), an owner's manual and service contract information, as non-limiting examples. Intelligent parameters associated with a functional key component such as, HVAC Equipment, may include the manufacturer, model number, capacity, efficiency rating, serial number, warranty start date, motor size, SEER rating, an owner's manual associated with the equipment, and service contract information.

Key components of the structure may have an identification device such as a two or three dimensional graphical code (such as a QR code label) a Radio Frequency Identification Chip (RFID) attached that is accessible to a user, such as a structure owner, structure builder or service technician. When scanned with an apparatus capable of reading the code, a user interface on a display of various types, such as a tablet, may use the associated identification, such as a QR code, to provide direct access to related information. In some examples, the display may show textual or tabular representations of related data.

In other examples, graphical data such as images, drawings, and the like may be displayed. In still further examples, both graphical and textual displays may be associated with the code. Although a QR code may provide an example, other identification technologies such as radio frequency ID, Internet of things (IoT) communication protocols with associated stored information, and other devices that can receive a signal and respond with stored information may be used. As well, numerous other types of graphical codes in addition to QR code may be read by a device and provide a connection between a key component, machinery, location and other identified aspect and associated data. In some examples, an image based code may be displayed using paints or pigments which are not visible to the human eye, such as in a non-limiting example ultraviolet pigments. In some other examples, a paint or pigment may not be visible until it is made to emit visible light by irradiating it with a particular band of electromagnetic radiation, such as, for example, ultraviolet light.

In some examples, key components may include doors, windows, masonry, roofing materials, insulation, HVAC equipment and machinery.

An automated Commercial Design and Monitoring (“RDM”) system may support dynamic updating of tracked aspects. For example, as a structure owner acquires new or additional key components, such as machinery, HVAC, plumbing additions, key components may be added into the As Built model and the key components may be tracked as a part of the model. Other aspects may be dynamically updated such as when additions are made to the building structure or rebuilding of internal structure is made as non-limiting examples.

Since the As Built model includes information in a database and dynamic model functionality exists that commences as a building structure is being constructed, the model may assume new support aspects to the construction process itself. For example, a benefit from the definition and utilization of many components within a Processing Facility utilizing the system herein includes the ability to pre-cut and/or pre-fabricate studs and framing, roofing cuts, masonry, under-slab plumbing, HVAC ductwork, electrical, and other such components. The dimensions of these various components may be dynamically updated based on an original model that may be compared to actual fabricated structure as realized on a building site. In some examples a structure builder may use a display interface associated with the system and model to display a comparison of an original set of building plans to a current structure at a point in time which may allow the builder to authorize any structural changes or variances to design and thereafter allow the description of following components to be dynamically adjusted as appropriate. The system may be of further utility to support various inspections that may occur during a building project which may associate detected variances with design expert review and approval. An inspector may be able to utilize the system as allowed on site or operate a window into the system from a remote location such as his office.

As the system is utilized during construction, orders for customized components may be placed. These customized components may be labeled and delivered to site, in an appropriate sequence, for assembly by carpenters. This may contribute to a minimization of waste at the worksite, as well as provide a work product that is entirely consistent with a pre-determined model which may have approved changes that are tracked. The result may improve the quality of the work product, and make it easier to generate the measured point-cloud 3D model.

Performance Tracking

In another aspect, the AVM system can autonomously and/or interactively obtain, store and process data that is provided to it by components of the Processing Facility as the structure is built, installed or additions are made to the structure. The generation, modeling, capture, use, and retention of data relating to Performances in specific equipment or in some cases aspects relating to the design of a facility, may be monitored by the system.

In some examples, Operational Performance may be assessed by processing sampled data with algorithms of various kinds. Feedback of the status of operation and of the structure as a whole or in part, as assessed by algorithmic analysis may be made to a structure owner or a structure builder. In addition, a variety of data points gathered via appropriate Sensors, visual and sound data may be recorded and stored and correlated to 3D models of the facility. Experiential Sensor readings may include, by way of non-limiting example: temperature, power usage, utilities used, consumables, product throughput, equipment settings, and equipment Performance measurement, visual and audible data. Techniques to record data points may involve the use of one or more of: electronic Sensors, electro-mechanical Sensors, CCD capture devices, automated inspection equipment, video camera arrays and audio microphones and arrays of audio microphones for the capture and processing of data that may be used to generate visualizations of actual conditions, either on site or at a remote location. In addition, data may be collected, retained, analyzed, and referenced to project facility Performance.

In some examples, data may also be combined with manufacturer equipment specifications and historical data to model expectations related to actual operation of the structure and property aspects.

Virtual Maintenance Support

A 3D model of structure, such as a commercial structure, which may be integrated with information related to the key components and laser scanned location information, may be made available to the structure owner/structure builder through a computer, an iPad or tablet, or smart device. The resulting system may be useful to support virtual maintenance support.

The three dimensional model may support enhancement to the two dimensional views that are typical of paper based drawings. Although three dimensional renderings are within the scope of information delivered in paper format, a three dimensional electronic model may render dynamic views from a three dimensional perspective. In some examples, the viewing may performed with viewing apparatus that allows for a virtual reality viewing.

In some examples, a viewing apparatus, such as a tablet or a virtual reality headset, may include orienting features that allow a user such as a structure owner, structure builder, inspector, engineer, designer or the like to view aspects of a model based upon a location, a direction, a height and an angle of view. A current view may be supplemented with various other information relating to features presented in the view. In some examples, the interface may be accessible through a virtual reality headset, computer, or mobile device (such as an iPad, tablet, or phone), as non-limiting examples. Utilizing a device equipped with an accelerometer, such as a virtual reality headset or mobile device, as non-limiting examples, a viewable section of the model may be displayed through the viewing medium (whether on a screen, or through a viewing lens), where the viewer's perspective changes as the accelerometer equipped device moves, allowing them to change their view of the model. The viewer's Vantage Point may also be adjusted, through a certain user input method, or by physical movement of the user, as non-limiting examples.

The presented view may be supplemented with “hidden information”, which may include for example, depictions of features that were scanned before walls were installed including pipes, conduits, ductwork and the like. Locations of beams, headers, studs and building structure may be depicted. In some examples, depiction in a view may include a superposition of an engineering drawing with a designed location, in other examples images of an actual structure may be superimposed upon the image based upon As Built scans or other recordations.

In a dynamic sense, display may be used to support viewing of hypothetical conditions such as rerouted utilities, and rebuild walls and other such structure. In some examples, graphical or text based data may be superimposed over an image and be used to indicate specifications, Performance aspects, or other information not related to location, shape and size of features in the image.

As presented above, an image may allow for a user to “see through walls” as the augmented reality viewing device simulates a section of a model associated with a space displayed via the virtual reality viewing device. The viewer's perspective may change as an accelerometer in the virtual reality viewing device moves. A user may also change a view of the AVM, to include different layers of data available in the AVM. The viewer's Vantage Point may also be adjusted by moving about a physical space that is represented by the model. To achieve this, it may be possible to incorporate positioning hardware directly into a building represented by the virtual model. The positioning hardware may interface with an augmented reality device for positioning data to accurately determine the viewing device's orientation and location with millimeter precision. The positioning hardware may include, for example a radio transmitter associated with a reference position and height. Altitude is differentiated from height unless specifically referenced since the relative height is typically more important.

Accordingly, a user may access the AVM on site and hold up a smart device, such as an iPad or other tablet, and use the smart device to generate a view inside a wall in front of which the smart device is positioned, based upon the AVM and the location, height and direction of the smart device position.

In some examples, through the use of an augmented reality device, it may also be possible to view data, such as user manuals, etc. of associated devices in the view of a user, simply by looking at them in the viewing interface. In other examples, there may be interactive means to select what information is presented on the view.

Various electronic based devices implementing of the present invention may also be viewed in a virtual reality environment without accelerometer such as a laptop or personal computer. A viewable section of a model may be displayed on a Graphical User Interface (GUI) and the viewer's Vantage Point may be adjusted, through a user input device.

The ability to track machinery and other components of a commercial system and store the components associated information, such as, for example user manuals and product specifications and part numbers, may allow for much more efficient use and maintenance of the components included within a structure. As well, the system model may also maintain structure owner manuals and warranties and eliminate the need for storage and tracking of hard copy manuals.

In a non-limiting example, if a structure owner/structure builder desires information related to an machinery, it may be found by positioning a device with a location determining the device within it in proximity to the machinery and accessing the parallel model in the Virtual Processing Facility such as by clicking on the machinery in the Virtual Processing Facility model or by scanning the Code label attached to machinery. In some examples, an internet of things equipped machine may have the ability to pair with a user's viewing screen and allow the system model to look up and display various information. Thus, the user may have access to various intelligent parameters associated with that machinery such as service records, a manual, service contract information, warranty information, consumables recommended for use such as detergents, installation related information, power hooked up and the like.

In some examples, an AVM system may include interfaces of various kinds to components of the commercial system. Sensors and other operational parameter detection apparatus may provide a routine feedback of information to the model system. Therefore, by processing the data-stream with various algorithms autonomous characterization of operating condition may be made. Therefore, the AVM system may provide a user with alerts when anomalies in system Performance are recognized. In some examples, standard structure maintenance requirements may be sensed or tracked based on usage and/or time and either notification or in some cases scheduling of a service call may be made. In some examples, the alert may be sent via text, email, or both. The structure user may, accordingly, log back into the Virtual Processing Facility to indicate completion of a maintenance task; or as appropriate a vendor of such service or maintenance may indicate a nature and completion of work performed.

By detecting operational status, a Virtual Processing Facility may take additional autonomous steps to support optimal operation of a commercial system. A Virtual Processing Facility may take steps to order and facilitate shipping of anticipated parts needed for a scheduled maintenance ahead of a scheduled date for a maintenance event (for example, shipping a filter ahead of time so the filter arrives prior to the date it is scheduled to be changed). In another example, a Virtual Processing Facility may recall notes from an Original Equipment Manufacturer (OEM) that could be communicated to a user through the Virtual Processing Facility. In still further examples, a Virtual Processing Facility may support a user involved in a real estate transaction by quantifying service records and Performance of a real property.

In still another aspect the AVM may establish a standard maintenance and warranty program based on manufacturers published data and the ability to advise structure owners of upcoming needs and/or requirements. In other examples, the model system may facilitate allowing for structure builders, rental companies, or maintenance companies to consolidate information for volume discounts on parts or maintenance items. The model system may also facilitate minimizing unnecessary time expenditure for structure builders hoping to minimize needless service calls for warranty issues, and allowing structure builders and rental companies attempting to sell a structure or a rental to demonstrate that care has been taken to maintain a structure.

Benefits derived from monitoring and tracking maintenance with a Virtual Processing Facility may include positively reassuring and educating lenders and/or lien holders that their investment is being properly cared for. In addition, insurance companies may use access to a Virtual Processing Facility to provide factual support that their risk is properly managed. In some examples, a data record in a Virtual Processing Facility model system and how an owner has cared for their commercial facility may be used by insurance companies or lenders to ensure that good care is being taken. Maintenance records demonstrating defined criteria may allow insurance companies to offer a structure owner policy discount, such as, for example, installation of an alarm system. Additionally, access to a Virtual Processing Facility may allow municipalities and utilities to use the info for accurate metering of utility usage without having to manually check; and peaks in utility demand may be more accurately anticipated.

In some examples, Virtual Processing Facility may also be used to assist with structure improvement projects of various types. In some examples, the structure improvement projects may include support for building larger additions and modifications, implementing landscaping projects. Smaller projects may also be assisted, including in a non-limiting example such a project as hanging a picture, which may be made safer and easier with the 3D “as-built” point cloud information. Hidden water piping, electrical conduits, wiring, and the like may be located, or virtually “uncovered”, based on the model database.

Optimization of Commercial Facilities

During construction of a structure corresponding to a Virtual Processing Facility, discrete features of the As Built structure may be identified via an identification device such as an IoT device or a QR code label. The ID device may be integrated to the feature or added during the build scope. Performance monitors may also be simultaneously installed to allow monitoring of Key Performance Indicators (KPIs) for selected features. In an example, an HVAC system may be added to a commercial facility during construction and a simultaneously a Performance monitor may be added to the HVAC system. The Performance monitor may be used to monitor various KPIs for an HVAC system. These KPIs may include outdoor air temperature, discharge air temperature, discharge air volume, electrical current, and the like. Similar monitoring capabilities may be installed to all machinery and utilities systems in a commercial facility. The combination of these numerous system monitors may allow for a fuller picture of the efficiency of operations of various systems.

Use of the Virtual Processing Facility, which may include data values contributed from communication of data from the various monitoring systems, may allow owners to receive periodic reports, such as in a non-limiting sense monthly emails which may show their current total energy consumption as well as a breakdown of what key components are contributing to the current total energy consumption.

The systems presented herein may be used by owners and facility managers to make decisions that may improve the cost effectiveness of the commercial system. An additional service for Owners may allow the structure owner to tap into energy saving options as their structure ages. As an example, if a more efficient HVAC system comes on the market, which may include perhaps a new technology node, the user may receive a “Savings Alert”. Such an alert may provide an estimated energy savings of the recommended modification along with an estimate of the cost of the new system. These estimates may be used to generate a report to the owner of an estimated associated return-on-investment or estimated payback period should the structure owner elect to replace their HVAC system.

In some examples, a AVM of a Virtual Processing Facility may set a threshold value for the required ROI above which they may be interested in receiving such an alert with that ROI is achieved. This information will be based on data derived from actual operating conditions and actual historical usage as well as current industry information. Predictive maintenance and energy savings to key systems via Smart Structure Total Cost of Ownership (“TCO”) branded Sensors.

Aggregating Data from Multiple Residences

With the ability to collect and utilize relevant structure information with the model system, the aggregation of data and efficiency experience from numerous commercial systems may allow for analysis of optimization schemes for various devices, machinery and other structure components that includes real installed location experience. Analysis from the aggregated data may be used to provide feedback to equipment manufacturers, building materials fabricators and such suppliers.

In some examples, business models may include providing anonymous and aggregated data to original equipment manufacturers as a service model to give the OEMS an ability to utilize more data to monitor and improve their products. In some examples, OEM advertising may be afforded access through the model system. Manufacturers may have an additional side benefit motivating the use of this data related to improving their equipment cost effectives and reliability in order to minimize warranty cost. Such optimized Performance may also provide benefits to both structure owners and builders to support their ability to track actual warranty information, power cost, and overall Performance of a structure.

Methods and Apparatus

Referring to FIGS. 3A-3F, an illustration of the collection of data by scanning a facility during its construction is provided. In FIG. 3A, a depiction of a site for building a facility structure is illustrated. The depiction may represent an image that may be seen from above the site. Indications of property boundaries such as corners 301 and property boarders 302 are represented and may be determined based on site scanning with property markings from site surveys or may be entered based on global coordinates for the property lines. An excavated location 303 may be marked out. Roadways, parking and/or loading areas 304 may be located. Buried utilities such as buried telephone 305, buried electric 306, buried water and sewer 307 are located in the model as illustrated. In some examples, such other site service as a buried sprinkler system 308 may also be located.

Referring to FIG. 3B the excavated location 303 may be scanned or imaged to determine the location of foundation elements. In some non-limiting examples, a foundational footing 321 along with buried utilities 322 is illustrated. The buried utilities may include such utilities as electric lines, water supply whether from a utility or a well on location, sewer or septic system lines, telecommunications lines such as telephone, cable and internet. Other footing elements 323 may be located at structural requiring locations as they are built. In some examples a scanning system may provide the locational orientation relative to site orientation markings. In other examples, aerial imagery such as may be obtained with a drone may be used to convert features to accurate location imagery.

Referring to FIG. 3C a wall 331 of the Processing Facility in the process of build is illustrated. The structure may be scanned by a scanning element 330. In some examples, a laser three dimensional scanner may be used. The wall may have supporting features like top plates 333, headers 336, studs 332, as well as internal items such as pipes 334, electrical conduits and wires 335. There may be numerous other types of features within walls that may be scanned as they occur such as air ducts, data cables, video cables, telephone cables, and the like.

Referring to FIG. 3D the wall may be completed with structure components behind wall facing 340 may no longer be visible. Electrical outlets 341 and door structures 342 may be scanned by a scanning element 330.

Referring to FIG. 3E internal components such as machinery may be installed. As a non-limiting example, a machine 350 may be installed and the resulting three dimensional profiles may be scanned by a scanning element 330. In some examples, an operational monitor 351 may be attached to the machinery. In some examples, an operational monitor may be part of the machinery. The operational monitor may have the ability to communicate 352 data to various receivers that may be connected to the model system of the residence. In some examples, key structural components, such as doors, may have identifying devices such as a QR label 353. The label may be visible or painted into the structure with non-visible paint. The identifying devices may provide information related to the device itself and warrantees of the device as non-limiting examples.

The model may include the various structure elements hidden and visible and may be used to create output to a display system of a user. Referring to FIG. 3F an example display is illustrated. The various non-visible layers may be shown by rendering the covering layers with a transparency. Thus the display shows the machine profile 350 as well as the internal features that may be concealed like pipes 334, electrical conduits with wires 335, and headers 336 as examples.

Referring to FIG. 3G, an illustration of feedback of the model system is illustrated. A wall that has been scanned with an HVAC unit 360 may include a Performance Monitor 351 which may communication various information wirelessly 352. The communication may be received at an antenna 370 of a router 371 within the commercial facility. The commercial facility may be interconnected through the internet 372 to a web located server 373 which processes the communication. The web located server 373 also can include the various model data about the commercial facility and it can provide composite displays that can summarize the structure as well as the operational Performance of the HVAC unit 360. It may aggregate the various data into textual and graphic reports. In some examples it may communicate these reports back through internet connections. In other examples, wireless Smart Device communications may be sent to cellular towers 374 which may transmit 375 to a Smart Device 376 of a user associated with the commercial facility.

Referring to FIG. 3H an illustration of a virtual reality display in concert with the present invention is illustrated. A machinery 350 of the commercial facility may communicate information to the model server. A user 380 may receive may an integrated communication from the server. The resulting communication may be provided to a virtual reality headset 381. The virtual reality headset may provide a display 382 to the user that provides a three-dimensional view of the physical data as well as simulated imagery that may allow views through objects to hidden elements behind the object. As well, a heads up type display of information about an object may be superimposed.

Referring now to FIG. 4A, method steps that may be implemented in some embodiments of the present invention are illustrated. At method step 401, Deployment aspects may be specified for a Processing Facility and incorporated into a virtual model, such as an AVM discussed above. Deployment aspects may include for example, a purpose for an As Built structure that is built based of the AVM. The purpose may include, by way of non-limiting example, one or more of: manufacturing, processing, data processing, health care, research, assembly, shipping and receiving, prototyping and the like.

Deployment aspects may also include a level of use, such continual, shift schedule or periodic. A climate in which the structure will be placed may also be considered in the Deployment aspects. Climate may include one or more of: four seasons; primarily winter; tropical, desert; exposed to salt air; and other environmental factors.

At method step 402, a virtual model, such as an AVM is digitally created according to the Deployment aspects of the model. The AVM may include improvements to a real estate parcel and a structure that will be placed on the real estate parcel, as well as where a structure may be located upon the parcel.

At method step 403, Performance aspects of machinery that may be included in the AVM may be digitally modeled and may include a level of use of the machinery and an expected satisfaction of the machinery as deployed according to the Deployment aspects. Maintenance expectations, including a number of repair calls and a preventive maintenance schedule may also be modeled and associated costs.

At method step 404, Performance aspects of equipment that may be included in the AVM may be digitally modeled and may include a level of use of the equipment and an expected satisfaction of the machinery as deployed according to the Deployment aspects. Maintenance expectations, including a number of repair calls and a preventive maintenance schedule may also be modeled and associated costs.

At method step 405, As Built aspects of a structure are recorded as discussed herein, preferably recordation of As Built aspects begins as construction begins and continues throughout the existence of the structure.

At method step 406, the physical structure may be identified via a location. A physical location may include, for example, Cartesian Coordinates, such as Latitude and Longitude coordinates, GPS coordinates, or other verifiable set of location parameters. In addition, more exact location specifications may include survey designations.

At method step 407, a position within or proximate to the Processing Facility may be determined via positioning identifiers. The position within or proximate to the Processing Facility may be determined.

At method step 408, an AVM may be identified and accessed via the physical location. Once an appropriate AVM is accessed, a particular portion of the AVM may be presented via a GUI based upon the position within the Processing Facility (or proximate to the Processing Facility) and a direction, height and angle of view. The position may be determined relative to location identifiers. Height may be determined via electronic devices, such as a smart device, or via triangulation referencing the location identifiers (locations identifiers are discussed more fully above and below).

At method step 409 an update may be made to a physical Processing Facility and at method step 410, the update to the physical structure may be recorded and reflected in the AVM.

Referring to FIG. 4B, a method flow diagram for commercial monitoring and maintenance is illustrated. At 411 a user may obtain a scanning device or devices that may scan a building site. At 412, the user or a service of the user may mark property boundaries of the commercial site. At 413, work on the commercial site may continue with the excavation of a building base and the laying down of utilities and other buried services. At 414, the scanning device is used to scan the location of the various aspects of the building site. At 415, work may continue with the laying of footings and foundations and other such foundational building activities. At 416, scanning of the footings and foundations may be accomplished. At 417, a commercial structure may be framed and features such as pipe conduit, electrical wiring communications wiring and the like may be added. At 418, the building site may again be scanned to locate the various elements. The framing of the residence may commence along with running of pipe, wiring, conduits, ducts and various other items that are located within wall structures. Before coverings are placed on walls, the framed structure may be scanned at 418. Thereafter, the framed structure may be enclosed with walls 419.

Referring to FIG. 4C a method flow diagram for commercial structure monitoring and maintenance is illustrated. In this flow diagram, a Processing Facility may already be built and may have various data layers already located in the model system. At 421, machinery may be added to the Processing Facility. At 422, an ID tag, or a QR tag, or and RFID tag or an internet of things device may be associated with the machinery and may be programmed into the model system. At 423, the model system may be interfaced to the machinery ID and into the Processing Facility model. At 424, a scanning step may be used to input three dimensional structure data at the installed location into the model system. At 425, an operational monitor function of the device may be added or activated. At 426, operational data may be transferred from the operational monitor to the server with the Processing Facility model.

At 427, algorithms running on a server of the model system may determine an operational improvement opportunity based on calculations performed on the data from the operational monitor. At 428 a user may query the operational data of the machinery for information on its warranty. At 429, the model system may initiate an order for a service part and may schedule a service visit to make a repair based upon analysis of the operational data. The various steps outlined in the processing flow may be performed in different orders. In some examples additional steps may be performed. In some examples, some steps may not be performed.

In some embodiments, the present invention includes a method of tracking attainment of a stated Performance Level relating to a Processing Facility, including: a) determining a geographic position of a Processing Facility via a global positioning system device in a smart device proximate to the Processing Facility; b) identifying a digital model of the Processing Facility based upon the geographic position of the Processing Facility, the digital model comprising virtual representation of structural components included in the Processing Facility; c) referencing multiple positioning reference devices within the Processing Facility; d) measuring a distance to at least three of the multiple positioning reference devices from a point of measurement; e) calculating a position within the Processing Facility, the calculation based upon a relative distance of the at least three positioning reference devices to the point of measurement and a triangulation calculation; f) calculating an elevation of the point of measurement; g) measuring a first state within the Processing Facility with a sensor; h) specifying a location of the first state within the Processing Facility via reference to the position of the point of measurement and the elevation of the point of measurement; i) recording a first time designation for the step of measuring a first state within the Processing Facility with a sensor; and i) correlating the first state within the Processing Facility and the first time designation attainment of the stated Performance Level.

The geographic position may be calculated with a GPS reading from within the Processing Facility. Measuring a distance to the at least three of the positioning reference devices may include, one or more of: relative signal strength received from wireless transmissions emanating from the at least three positioning reference devices; time of arrival of radio signals of wireless transmissions emanating from the at least three positioning reference devices measuring a distance to the at least three positioning reference devices comprises time difference of arrival of radio signals of wireless transmissions emanating from the at least three reference positioning devices.

The above steps may be repeated for at least a second state and a second time designation, and in preferred embodiments multiple more states and time designations.

A state may include, for example, one or more of: a vibration measured with an accelerometer; a temperature of at least a portion of the structure; an electrical current measurement to equipment installed in the Processing Facility, a number of cycles of operation of equipment installed in the Processing Facility; a number of cycles of operation of an machinery installed in the Processing Facility; an electrical current measurement to an machinery installed in the Processing Facility; a vibration associated with movement of an occupant of the Processing Facility.

A vibration pattern may be associated with a specific occupant and tracking the movement of the specific occupant through the structure may be based upon measured vibration patterns. Similarly, a vibration pattern may be associated with a particular activity of a specific occupant and the activity of the specific occupant may be tracked within the structure based upon measured vibration patterns.

A Performance Level may include one or more of: operating the Processing Facility for a term of years within a threshold use of energy; operating the Processing Facility for a term of years within a threshold number of repairs; and operating the Processing Facility for a term of years within a threshold budgetary cost.

FIG. 5 illustrates location and positioning identifiers 501-504 that may be deployed in a Processing Facility according to some embodiments of the present invention to determine a user position 500 within or proximate to the Processing Facility 505. Positioning identifiers may include a device that is fixed in a certain location and may be used to determine via calculation a position of a user with a tablet, smart phone or other network access device able to recognize the position identifiers. The position identifiers 501-504 may include devices, such as, for example, a radio transmitter, a light beacon, or an image recognizable device. A radio transmitter may include a router or other WiFi device. In some embodiments, a position identifier may include a WiFi router that additionally provides access to a distributed network, such as the Internet. Cartesian Coordinates, such as a GPS position 506, may be utilized to locate and identify the Processing Facility 506.

A precise location may be determined via triangulation based upon a measured distance from three 501-503 or more position identifiers 501-504. For example a radio transmission or light signal may be measured and compared from the three reference position identifiers 501-503. Other embodiments may include a device recognizable via image analysis and a camera or other Image Capture Device, such as a CCD device, may capture an image of three or more position identifiers 501-504. Image analysis may recognize the identification of each of three or more of the position identifiers 501-504 and a size ratio of the respective image captured position identifiers 501-504 may be utilized to calculate a precise position. Similarly, a height designation may be made via triangulation using the position identifiers as reference to a known height or a reference height.

Referring now to FIG. 6 an automated controller is illustrated that may be used to implement various aspects of the present invention, in various embodiments, and for various aspects of the present invention, controller 600 may be included in one or more of: a wireless tablet or handheld device, a server, a rack mounted processor unit. The controller may be included in one or more of the apparatus described above, such as a Server, and a Network Access Device. The controller 600 includes a processor unit 620, such as one or more semiconductor based processors, coupled to a communication device 610 configured to communicate via a communication network (not shown in FIG. 6). The communication device 610 may be used to communicate, for example, with one or more online devices, such as a personal computer, laptop, or a handheld device.

The processor 620 is also in communication with a storage device 630. The storage device 630 may comprise any appropriate information storage device, including combinations of magnetic storage devices (e.g., magnetic tape and hard disk drives), optical storage devices, and/or semiconductor memory devices such as Random Access Memory (RAM) devices and Read Only Memory (ROM) devices.

The storage device 630 can store a software program 640 with executable logic for controlling the processor 620. The processor 620 performs instructions of the software program 640, and thereby operates in accordance with the present invention. The processor 620 may also cause the communication device 610 to transmit information, including, in some instances, control commands to operate apparatus to implement the processes described above. The storage device 630 can additionally store related data in a database 650 and database 660, as needed.

Referring now to FIG. 7, a block diagram of an exemplary mobile device 702. The mobile device 702 comprises an optical capture device 708 to capture an image and convert it to machine-compatible data, and an optical path 706, typically a lens, an aperture or an image conduit to convey the image from the rendered document to the optical capture device 708. The optical capture device 708 may incorporate a Charge-Coupled Device (CCD), a Complementary Metal Oxide Semiconductor (CMOS) imaging device, or an optical Sensor 724 of another type.

A microphone 710 and associated circuitry may convert the sound of the environment, including spoken words, into machine-compatible signals. Input facilities may exist in the form of buttons, scroll wheels, or other tactile Sensors such as touch-pads. In some embodiments, input facilities may include a touchscreen display.

Visual feedback to the user is possible through a visual display, touchscreen display, or indicator lights. Audible feedback 734 may come from a loudspeaker or other audio transducer. Tactile feedback may come from a vibrate module 736.

A motion Sensor 738 and associated circuitry convert the motion of the mobile device 702 into machine-compatible signals. The motion Sensor 738 may comprise an accelerometer that may be used to sense measurable physical acceleration, orientation, vibration, and other movements. In some embodiments, motion Sensor 738 may include a gyroscope or other device to sense different motions.

A location Sensor 740 and associated circuitry may be used to determine the location of the device. The location Sensor 740 may detect Global Position System (GPS) radio signals from satellites or may also use assisted GPS where the mobile device may use a cellular network to decrease the time necessary to determine location. In some embodiments, the location Sensor 740 may use radio waves to determine the distance from known radio sources such as cellular towers to determine the location of the mobile device 702. In some embodiments these radio signals may be used in addition to GPS.

The mobile device 702 comprises logic 726 to interact with the various other components, possibly processing the received signals into different formats and/or interpretations. Logic 726 may be operable to read and write data and program instructions stored in associated storage or memory 730 such as RAM, ROM, flash, or other suitable memory. It may read a time signal from the clock unit 728. In some embodiments, the mobile device 702 may have an on-board power supply 732. In other embodiments, the mobile device 702 may be powered from a tethered connection to another device, such as a Universal Serial Bus (USB) connection.

The mobile device 702 also includes a network interface 716 to communicate data to a network and/or an associated computing device. Network interface 716 may provide two-way data communication. For example, network interface 716 may operate according to the internet protocol. As another example, network interface 716 may be a local area network (LAN) card allowing a data communication connection to a compatible LAN. As another example, network interface 716 may be a cellular antenna and associated circuitry which may allow the mobile device to communicate over standard wireless data communication networks. In some implementations, network interface 716 may include a Universal Serial Bus (USB) to supply power or transmit data. In some embodiments other wireless links may also be implemented.

As an example of one use of mobile device 702, a reader may scan some coded information from a location marker in a facility with the mobile device 702. The coded information may include for example a hash code, bar code, RFID or other data storage device. In some embodiments, the scan may include a bit-mapped image via the optical capture device 708. Logic 726 causes the bit-mapped image to be stored in memory 730 with an associated time-stamp read from the clock unit 728. Logic 726 may also perform optical character recognition (OCR) or other post-scan processing on the bit-mapped image to convert it to text. Logic 726 may optionally extract a signature from the image, for example by performing a convolution-like process to locate repeating occurrences of characters, symbols or objects, and determine the distance or number of other characters, symbols, or objects between these repeated elements. The reader may then upload the bit-mapped image (or text or other signature, if post-scan processing has been performed by logic 726) to an associated computer via network interface 716.

As an example of another use of mobile device 702, a reader may capture some text from an article as an audio file by using microphone 710 as an acoustic capture port. Logic 726 causes audio file to be stored in memory 730. Logic 726 may also perform voice recognition or other post-scan processing on the audio file to convert it to text. As above, the reader may then upload the audio file (or text produced by post-scan processing performed by logic 726) to an associated computer via network interface 716.

A directional sensor 741 may also be incorporated into the mobile device 702. The directional device may be a compass and be based upon a magnetic reading, or based upon network settings.

In the following sections, detailed descriptions of examples and methods of the invention will be given. The description of both preferred and alternative examples though through are exemplary only, and it is understood that to those skilled in the art that variations, modifications and alterations may be apparent. It is therefore to be understood that the examples do not limit the broadness of the aspects of the underlying invention as defined by the claims.

Referring now to FIG. 8, exemplary steps that may be performed in some aspects of the present invention are illustrated. At step 801, a processor may generate an AVM model of a processing facility. The AVM model may be based upon a physical layout of the processing facility and include a layout of each item of machinery, equipment as well as facility features. At step 802, the AVM may receive data indicative of one or more performance metrics. Data may include data generated via a sensor and/or input by a user. In some examples, data may include performance metrics, utility cost, maintenance cost and replacement cost.

At step 803, a data connection between a deployed facility and an AVM may be automated to generate and transmit data to the model on an automated basis without human intervention or artificial delay. All or some data may be stored in a storage. At step 804, the AVM may access received and/or historical data from the same or other AVM models. At step 805. Artificial Intelligence routines or other logic may integrate relevant indices, including one or more of: geographic location, labor organization, market conditions, labor costs, physical conditions, property status or data descriptive of other variables.

At step 806, an AVM may generate a value for build and deployment cost, and at step 807 the AVM may include utility and consumables cost. At step 808 an AVM may generate one or more of: predicted and actual quantifications from the structure; energy consumption and process throughput.

Referring now to FIG. 9A, an exemplary perspective graph 900 comprising three separate perspective points 925, 945, 965 is illustrated. In some aspects, as illustrated in FIG. 9B, a wearable display 905 may be configured to detect eye movement of the wearer 915, which may be calibrated. For example, such as illustrated in FIG. 9B, a neutral, forward-looking eye position 920 may be established as the center point of the axes 910 (0, 0), which may establish a view along the positive z-axis. As a further illustrative example in FIG. 9C, once calibrated, a shift in eye position 940 to look up and left may change a view from the vantage point and be transmitted to the AVM to access another portion of the AVM. As an illustrative example, as shown in FIG. 9D, a user may look right, and the eye position 960 may shift along the positive x-axis.

In some aspects, the wearable display 905 may comprise a set of goggles or glasses, wherein the goggles or glasses may comprise one or more lenses. For example, a single wrapped lens may allow a user to experience panoramic views. Alternately, dual lenses may provide different image data, wherein the combined images may allow the user to have stereoscopic perception of the performance event. In still further embodiments, the wearable display 905 may comprise a helmet, which may allow for more detailed immersion. For example, a helmet may allow for temperature control, audio isolation, broader perspectives, or combinations thereof.

Referring now to FIGS. 10A-10C, exemplary horizontal changes in viewing areas are illustrated. In some embodiments, the wearable display may comprise an accelerometer configured to detect head movement. Similarly to the eye position detection, the accelerometer may be calibrated to the natural head movements of a user 1000. In some embodiments, the calibration may allow the user to tailor the range to the desired viewing area. For example, a user may be able to move their head 110° comfortably, and the calibration may allow the user to view the entire 180° relative the natural 110° movement.

As illustrated in FIG. 10A, a neutral head position 1020 of the wearable display may allow the user 1000 to view a forward-looking perspective 1025. As illustrated in FIG. 10B, a right head position 1040 of the wearable display may allow the user 1000 to view a rightward-looking perspective 1045. As illustrated in FIG. 10C, a left head position 1060 of the wearable display may allow the user 1000 to view a leftward-looking perspective 1065.

Referring now to FIGS. 11A-11C, exemplary vertical changes in viewing areas are illustrated. Similarly to FIGS. 10A-10C, in some embodiments, the wearable display may be configured to detect vertical motions. In some aspects, a user may look up to shift the viewing area to a range in the positive y axis grids, and user may look down to shift the viewing area to a range in the negative y axis grids. In some embodiments, the wearable display may be configured to detect both horizontal and vertical head motion, wherein the user may be able to have almost a 270° viewing range.

As illustrated in FIG. 11A, a neutral head position 1120 of the wearable display may allow the user 1100 to view a forward-looking perspective 1125. As illustrated in FIG. 11B, an up head position 1140 of the wearable display may allow the user 1100 to view an upward-looking perspective 1145. As illustrated in FIG. 11C, a down head position 1160 of the wearable display may allow the user 1100 to view a downward-looking perspective 1165.

In still further embodiments, the wearable display may be able to detect 360° of horizontal movement, wherein the user may completely turn around and change the neutral viewing range by 180°. In some aspects, the wearable display may be configured to detect whether the user may be sitting or standing, which may shift the perspective and viewing area. In some implementations, a user may be allowed to activate or deactivate the motion detection levels, based on preference and need. For example, a user may want to shift between sitting and standing throughout the experience without a shift in perspective. In some implementations, the wearable display may further comprise speakers, wherein audio data may be directed to the user.

In some embodiments, the wearable display may allow for immersion level control, wherein a user may adjust the level of light and transparency of the wearable display and/or frames. In some aspects, the lenses of the wearable display may comprise an electrically active layer, wherein the level of energy may control the opacity. For example, the electrically active layer may comprise liquid crystal, wherein the energy level may control the alignment of the liquid crystal. Where a user may prefer a fully immersive viewing experience, the lenses may be blacked out, wherein the user may see the video with minimal external visibility. Where a user may still prefer to have awareness or interactions beyond the video, the lenses and/or frames may allow for some light to penetrate or may allow for some transparency of the video.

Additional examples may include Sensor arrays, audio capture arrays and camera arrays with multiple data collection angles that may be complete 360 degree camera arrays or directional arrays, for example, in some examples, a Sensor array (including image capture Sensors) may include at least 120 degrees of data capture, additional examples include a Sensor array with at least 180 degrees of image capture; and still other examples include a Sensor array with at least 270 degrees of image capture. In various examples, data capture may include Sensors arranged to capture image data in directions that are planar or oblique in relation to one another.

Referring now to FIG. 12, methods and devices for determining a direction that may be referenced for one or both of data capture and AVM presentation of a particular portion of the virtual representation of the modeled structure. A User 1200 may position a Smart Device 1205 in a first position 1201 proximate to a portion of a structure for which a representation in the AVM the User 1200 wishes to retrieve and display. The first position 1201 of the Smart Device 1205 may be determined (as discussed herein via GPS and/or triangulation) and recorded. The User 1200 may then relocate the Smart Device 1205 to a second position 1202 in a general direction of the portion of a structure (illustrated as the Z direction) for which a representation in the AVM the User 1200 wishes to retrieve and display. In this manner, the AVM system (not shown in FIG. 12) and/or the Smart Device 1205 may generate a vector towards the portion of a structure for which a representation in the AVM the User 1200 wishes to retrieve and display.

In some embodiments, the vector may have a length determined by the AVM that is based upon a length of a next Feature in the AVM located in the direction of the generated vector. The vector will represent a distance 1203 from the second position 1202 to an item 1225 along the Z axis defined by a line between the first position 1201 and the second position 1202.

As illustrated, the change in the Z direction is associated with a zero change in the X and Y directions. The process may also include a second position 1205 that has a value other than zero in the X and/or Y directions.

In other embodiments, a User 1200 may deploy a laser, accelerometer, sound generator or other device to determine a distance from the Smart Device 1205 to the feature, such as a piece of equipment. Such unique methods of determining a location and direction of data capture may be utilized to gather data during construction of modeled buildings or other structures and during Deployment of the structures during the Operational Stage. An additional non-limiting example may include direction based identification; with a fixed location, or in tandem with a location means, a device may have capabilities to deduce orientation based information of the device. This orientation information may be used to deduce a direction that the device is pointing in. This direction based information may be used to indicate that the device is pointing to a specific piece of equipment 1225 that may be identified in the AVM.

In still other embodiments, a device with a controller and an accelerometer, such as mobile Smart Device 1205, may include a user display that allows a direction to be indicated by movement of the device from a determined location acting as a base position towards an As Built feature in an extended position. In some implementations, the Smart Device determines a first position 1201 based upon triangulation with the reference points. The process of determination of a position based upon triangulation with the reference points may be accomplished, for example via executable software interacting with the controller in the Smart Device, such as, for example by running an app on the Smart Devices 1205.

In combination with, or in place of directional movement of a Smart Device 1205 in order to quantify a direction of interest to a user, some embodiments may include an electronic and/or magnetic directional indicator that may be aligned by a user in a direction of interest. Alignment may include, for example, pointing a specified side of a device, or pointing an arrow or other symbol displayed upon a user interface on the device towards a direction of interest.

In a similar fashion, triangulation may be utilized to determine a relative elevation of the Smart Device as compared to a reference elevation of the reference points.

It should be noted that although a Smart Device is generally operated by a human user, some embodiments of the present invention include a controller, accelerometer, and data storage medium, Image Capture Device, such as a Charge Coupled Device (“CCD”) capture device and/or an infrared capture device being available in a handheld or unmanned vehicle.

An unmanned vehicle may include for example, an unmanned aerial vehicle (“UAV”) or ground level unit, such as a unit with wheels or tracks for mobility and a radio control unit for communication.

In some embodiments, multiple unmanned vehicles may capture data in a synchronized fashion to add depth to the image capture and/or a three dimensional and 4 dimensional (over time) aspect to the captured data. In some implementations, UAV position will be contained within a perimeter and the perimeter will have multiple reference points to help each UAV (or other unmanned vehicle) determine a position in relation to static features of a building within which it is operating and also in relation to other unmanned vehicles. Still other aspects include unmanned vehicles that may not only capture data but also function to perform a task, such as paint a wall, drill a hole, cut along a defined path, or other function. As stated throughout this disclosure, the captured data may be incorporated into an AVM.

In still other embodiments, captured data may be compared to a library of stored data using recognition software to ascertain and/or affirm a specific location, elevation and direction of an image capture location and proper alignment with the virtual model. Still other aspects may include the use of a compass incorporated into a Smart Device.

By way of non-limiting example, functions of the methods and apparatus presented herein may include one or more of the following factors that may be modeled and/or tracked over a defined period of time, such as, for example, an expected life of a build (such as, 10 years or 20 years).

Particular embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order show, or sequential order, to achieve desirable results. In certain implementations, multitasking and parallel processing may be advantageous. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the claimed invention. 

What is claimed is:
 1. A method of augmenting a virtual model of a commercial structure, the method comprising; selecting a virtual model file of a commercial structure based upon a geolocation of the commercial structure; identifying an Augmented Virtual Model of the commercial structure based upon the geolocation, the Augmented Virtual Model comprising virtual representation of structural aspects of the commercial structure, utility support within the commercial structure and technical specifications associated with a processing machine virtually located within the commercial structure; positioning a smart device at a first position, wherein the smart device is a mobile device; with the smart device at the first position, referencing multiple positioning reference devices within the commercial structure; determining a distance to at least three of the positioning reference devices from the smart device at the first position; generating a first X, Y and Z value of the smart device within the commercial structure based upon the determined distance of the at least three positioning reference devices to the smart device at the first position and a triangulation calculation; specifying a direction of interest via the User extending the smart device from the first position comprising a base position to a second position comprising an extended position; determining a distance to at least three of the positioning reference devices from the smart device at the second position; calculating a second X, Y and Z value of the smart device at the second position based upon a determined distance of the at least three positioning reference devices to the smart device at the second position and a triangulation calculation; generating a direction of interest based upon a directional vector originating at the first position X, Y and Z value and continuing through the second position X, Y and Z value; generating a user interface on the smart device, the user interface comprising a symbol indicating the direction of interest; indicating a Z distance of interest from a data capture position comprising one of: a) the first position X, Y and Z value, and b) the second X, Y and Z value of the smart device; performing a data capture routine from the data capture position, said data capture routine generating a digital record of a condition of the commercial structure noted at the distance of interest and the direction of interest from the data capture position of the smart device; transmit the digital record of a condition of the commercial structure noted at the Z distance of interest from the data capture position of the smart device to the Augmented Virtual Model; and generating a virtual representation of the digital record of the condition of the commercial structure within the Augmented Virtual Model at a virtual location based upon the data capture position and the distance of interest and the Z direction of interest.
 2. The method of claim 1 additionally comprising the step of transmitting the data capture position of the smart device, to the Augmented Virtual Model.
 3. The method of claim 2 wherein at least one of the multiple positioning reference devices comprises a first radio frequency transmitter fixedly attached to a first reference position within the commercial structure and the method further comprises the steps of: a) transmitting to the Augmented Virtual Model an identification of the first radio frequency transmitter; and b) transmitting an instruction to store an identification of the first radio frequency transmitter in relation to the performance of the data capture routine.
 4. The method of claim 3 wherein at least one other position referencing device comprises a second radio frequency transmitter fixedly attached to a second reference position and associated with an identification of the second radio frequency transmitter, and the method further comprises the steps of: a) transmitting to the Augmented Virtual Model the identification of the second radio frequency transmitter; and b) transmitting an instruction to store the identification of the second radio frequency transmitter in relation to the performance of the data capture routine.
 5. The method of claim 3 wherein at least one position referencing device comprises a visual indicator, and the method further comprises the steps of: a) transmitting to the Augmented Virtual Model an identification of the visual indicator; and b) transmitting an instruction to store the identification of the visual indicator in relation to the performance of the data capture routine.
 6. The method of claim 3 wherein the data capture routine comprises capture of image data with a field of capture at the distance of interest and the direction of interest from the data capture position of the smart device.
 7. The method of claim 6 additionally comprising the step of transmitting an instruction to incorporate the captured image into the Augmented Virtual Model at a virtual position congruent with the Z distance of interest and the direction of interest from the X, Y and Z position.
 8. The method of claim 7 wherein the image data capture is accomplished with a camera incorporated into the smart device.
 9. The method of claim 3 wherein the data capture routine comprises obtaining captured data from a sensor reading based upon a physical condition of the commercial structure measured at the distance of interest and the direction of interest from the X, Y and Z position of the smart device and the elevation of the smart device.
 10. The method of claim 9 wherein the physical condition comprises an indication of an amount of vibration.
 11. The method of claim 9 wherein the physical condition comprises one or both of a temperature and a humidity present in the commercial structure at the distance of interest and the direction of interest from the X, Y and Z position of the smart device.
 12. The method of claim 9 further comprising the steps of: associating the distance of interest and the direction of interest from the X, Y and Z position of the smart device within a physical embodiment of the commercial structure with a nomenclature; and displaying captured image data within the Augmented Virtual Model, based upon a user entry of a query including the nomenclature.
 13. The method of claim 12 further comprising the steps of: a) providing an index of nomenclatures; associating each nomenclature with a virtual position and direction within the Augmented Virtual Model; and b) displaying captured data based upon a user selection of nomenclature.
 14. The method of claim 13 additionally comprising the steps of: displaying one or more utility support apparatus in the Augmented Virtual Model based upon a physical location of a user interface display device used to display the Augmented Virtual Model.
 15. The method of claim 13 wherein the data capture routine comprises a state of one or more utility support apparatus comprising one or both of: plumbing and electrical components.
 16. The method of claim 15 wherein the method additionally comprises the step of updating technical specifications regarding one or more utility support apparatus based upon the captured data.
 17. The method of claim 16 wherein the step of updating technical specifications regarding the one or more utility support based upon the captured data comprises a change in type of one or both of plumbing components and electrical components.
 18. The method of claim 16 wherein the step of updating technical specifications regarding the one or more utility support based upon the captured data comprises a change in location of one or both of plumbing components and electrical components.
 19. The method of claim 16 additionally comprising the step of generating a volume of production of a machine located at the distance of interest and the direction of interest from the X, Y and Z position of the smart device within the physical embodiment of the commercial structure.
 20. The method of claim 16 additionally comprising the step of generating a quality of production of a machine located at the distance of interest and the direction of interest from the X, Y and Z position of the smart device of the smart device within the physical embodiment of the commercial structure. 